This study investigated the potential pulmonary toxicity of polycarbonate (PC) emissions from fused filament fabrication (FFF) three-dimensional printing (3DP) via inhalation in Sprague Dawley rats. Previously, our results demonstrated no significant pulmonary effects following exposure to a 0.5 mg/m(3) PC. A new exposure apparatus was developed that exposed animals at a concentration of 2.5 mg/m(3). Sixty rats were randomized into control (filtered air) and exposure groups (n = 30/group). Each group was further divided into five subgroups (n = 6/subgroup) with exposure durations of 1, 4, 8, 15, or 30 days (4 hr/day, 4 days/week). Following a 24-hr post-exposure period, body weight was measured, and blood samples were collected for hematological and biochemical analysis. Bronchoalveolar lavage fluid (BALF) was obtained from the right lung for cytology. The left lung and head/nasal tissues were preserved for histopathological evaluation. Lung deposition was estimated using the Multiple-Path Particle Dosimetry model, electron microscopy, and enhanced darkfield microscopy. In addition, filter samples were collected to measure bisphenol A. Exposure resulted in an estimated deposition of 0.28 µg/day within the alveoli and small airways. Microscopy indicated limited evidence of macrophage uptake. No significant changes were observed in BALF cell counts, lactate dehydrogenase activity, or hematological parameters. BALF levels of tissue inhibitor of metalloproteinases-1 and protein were elevated in the 30-day exposure group, although histopathology revealed no exposure-related changes in the lungs. In conclusion, this study found no marked pulmonary inflammation or toxicity in rats exposed to 2.5 mg/m(3) of PC 3D printing emissions for up to 30 days (4 hr/day).

Purpose: Pulmonary exposure to emissions from manipulating solid surface composite (SSC) materials has been associated with adverse health effects in humans and laboratory animals. Previous in vitro and in vivo investigations of SSC toxicity have been limited by particle delivery methods that do not fully recapitulate the workplace environment. This study sought to determine the acute SSC-induced pulmonary responses via whole-body inhalation exposure. Materials and Methods: A chamber for dust particle generation and an exposure system for characterization and animal exposures was constructed. The system successfully generated SSC at a concentration of 19.9 ± 1.5 mg/m(3). The aerosol count median aerodynamic diameter was 820 nm. First, C57BL/6 mice were exposed to SSC particles for 4 h (n = 6) or filtered air control followed by euthanasia either immediately or 24 h post-exposure. Lungs were analyzed for aluminum (Al) content using inductively coupled plasma atomic emission spectroscopy (ICP-AES) which measured a lung deposition of 19.13 ± 5.03 µg/g elemental Al, or approximately 64 µg/g SSC dust. Second, a group of mice (n = 9) was exposed to SSC particles at 20 mg/m(3) for 4 days, 4 h/day to assess the acute and sub-chronic pulmonary effects of SSC inhalation. Animals were euthanized at 1- and 56-days post-exposure. Results: Total estimated pulmonary deposition for these animals was 49.2 µg SSC dust/animal. No histopathologic changes were observed at any post-exposure time point; however, BALF total protein was increased at 1-day post-exposure. Conclusions: We conclude that exposure to dust from cutting SSC at this dose and post-exposure durations induces mild, transient inflammation.

Carbon dioxide (CO2) is the most significant greenhouse gas and one of the strategies to reduce CO2 emission is to convert CO2 into commercially valuable products. Currently, methods that can effectively capture and convert CO2 into carbonate nanomaterials, which have unique applications in various fields, have rarely been reported, and there are no universal methods that can capture and convert CO2 into different nano-carbonates. In this study, an innovative two-step strategy based on amino acids was developed to produce multiple different carbonate nanoparticles, including CaCO3, BaCO3, and Ag2CO3 nanoparticles with diameters of 70 nm, 50 nm, and 7 nm, respectively. Fundamentally important, the nuclear magnetic resonance data clearly demonstrated that it was the amino acids (e.g., glycine) that dictated the formation of carbonate nanoparticles. In the presence of amino acids, a competition in forming nanoparticles and microparticles was observed, and the formation of nanoparticles was proportional to the carbamate formed from CO2 reacting with amino acids. In the absence of amino acids, carbonate microparticles (∼ 2 µm) were formed. © 2024 Elsevier B.V.

IMPORTANCE: Although influenza vaccination has been found to be safe in pregnancy, few studies have assessed repeated influenza vaccination over successive pregnancies, including 2 vaccinations in a year, in terms of adverse perinatal outcomes. OBJECTIVE: To examine the association of seasonal influenza vaccination across successive pregnancies with adverse perinatal outcomes and whether the association varies by interpregnancy interval (IPI) and vaccine type (quadrivalent or trivalent). DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study included individuals with at least 2 successive singleton live-birth pregnancies between January 1, 2004, and December 31, 2018. Data were collected from the Vaccine Safety Datalink, a collaboration between the Centers for Disease Control and Prevention and integrated health care organizations. Data analysis was performed between January 8, 2021, and July 17, 2024. EXPOSURES: Influenza vaccination was identified using vaccine administration codes. The vaccinated cohort consisted of people who received influenza vaccines during the influenza season (August 1 through April 30) in 2 successive pregnancies. The comparator cohort consisted of people identified as unvaccinated during both pregnancies. MAIN OUTCOMES AND MEASURES: Main outcomes were risk of preeclampsia or eclampsia, placental abruption, fever, preterm birth, preterm premature rupture of membranes, chorioamnionitis, and small for gestational age among individuals with and without vaccination in both pregnancies. Adjusted relative risks (RRs) from Poisson regression were used to assess the magnitude of associations. The associations with adverse outcomes by IPI and vaccine type were evaluated. RESULTS: Of 82 055 people with 2 singleton pregnancies between 2004 and 2018, 44 879 (54.7%) had influenza vaccination in successive pregnancies. Mean (SD) age at the start of the second pregnancy was 32.2 (4.6) years for vaccinated individuals and 31.2 (5.0) years for unvaccinated individuals. Compared with individuals not vaccinated in both pregnancies, vaccination in successive pregnancies was not associated with increased risk of preeclampsia or eclampsia (adjusted RR, 1.10; 95% CI, 0.99-1.21), placental abruption (adjusted RR, 1.01; 95% CI, 0.84-1.21), fever (adjusted RR, 0.87; 95% CI, 0.47-1.59), preterm birth (adjusted RR, 0.83; 95% CI, 0.78-0.89), preterm premature rupture of membranes (RR, 1.00; 95% CI, 0.94-1.06), chorioamnionitis (adjusted RR, 1.03; 95% CI, 0.90-1.18), or small for gestational age birth (adjusted RR, 0.99; 95% CI, 0.93-1.05). IPI and vaccine type did not modify the observed associations. CONCLUSIONS AND RELEVANCE: In this large cohort study of successive pregnancies, influenza vaccination was not associated with increased risk of adverse perinatal outcomes, irrespective of IPI and vaccine type. Findings support recommendations to vaccinate pregnant people or those who might be pregnant during the influenza season.

IMPORTANCE: People with HIV (PWH) may be at increased risk for severe outcomes with COVID-19 illness compared with people without HIV. Little is known about COVID-19 vaccination coverage and factors associated with primary series completion among PWH. OBJECTIVES: To evaluate COVID-19 vaccination coverage among PWH and examine sociodemographic, clinical, and community-level factors associated with completion of the primary series and an additional primary dose. DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study used electronic health record data to assess COVID-19 vaccination information from December 14, 2020, through April 30, 2022, from 8 health care organizations of the Vaccine Safety Datalink project in the US. Participants were adults diagnosed with HIV on or before December 14, 2020, enrolled in a participating site. MAIN OUTCOMES AND MEASURES: The percentage of PWH with at least 1 dose of COVID-19 vaccine and PWH who completed the COVID-19 vaccine primary series by December 31, 2021, and an additional primary dose by April 30, 2022. Rate ratios (RR) and 95% CIs were estimated using Poisson regression models for factors associated with completing the COVID-19 vaccine primary series and receiving an additional primary dose. RESULTS: Among 22 058 adult PWH (mean [SD] age, 52.1 [13.3] years; 88.8% male), 90.5% completed the primary series by December 31, 2021. Among 18 374 eligible PWH who completed the primary series by August 12, 2021, 15 982 (87.0%) received an additional primary dose, and 4318 (23.5%) received a booster dose by April 30, 2022. Receipt of influenza vaccines in the last 2 years was associated with completion of the primary series (RR, 1.17; 95% CI, 1.15-1.20) and an additional primary dose (RR, 1.61; 95% CI, 1.54-1.69). PWH with uncontrolled viremia (HIV viral load ≥200 copies/mL) (eg, RR, 0.90 [95% CI, 0.85-0.95] for viral load 200-10 000 copies/mL vs undetected or <200 copies/mL for completing the primary series) and Medicaid insurance (eg, RR, 0.89 [95% CI, 0.87-0.90] for completing the primary series) were less likely to be fully vaccinated. By contrast, greater outpatient utilization (eg, RR, 1.07 [95% CI, 1.05-1.09] for ≥7 vs 0 visits for primary series completion) and residence in counties with higher COVID-19 vaccine coverage (eg, RR, 1.06 [95% CI, 1.03-1.08] for fourth vs first quartiles for primary series completion) were associated with primary series and additional dose completion (RRs ranging from 1.01 to 1.21). CONCLUSIONS AND RELEVANCE: Findings from this cohort study suggest that, while COVID-19 vaccination coverage was high among PWH, outreach efforts should focus on those who did not complete vaccine series and those who have uncontrolled viremia.

COVID-19 vaccinations protect against severe illness and death, but associations with post-COVID conditions (PCC) are less clear. We aimed to evaluate the association between prior COVID-19 vaccination and new-onset PCC among individuals with SARS-CoV-2 infection across eight large healthcare systems in the United States. This retrospective matched cohort study used electronic health records (EHR) from patients with SARS-CoV-2 positive tests during March 2021-February 2022. Vaccinated and unvaccinated COVID-19 cases were matched on location, test date, severity of acute infection, age, and sex. Vaccination status was ascertained using EHR and integrated data on externally administered vaccines. Adjusted relative risks (RRs) were obtained from Poisson regression. PCC was defined as a new diagnosis in one of 13 PCC categories 30 days to 6 months following a positive SARS-CoV-2 test. The study included 161,531 vaccinated COVID-19 cases and 161,531 matched unvaccinated cases. Compared to unvaccinated cases, vaccinated cases had a similar or lower risk of all PCC categories except mental health disorders (RR: 1.06, 95% CI: 1.02-1.10). Vaccination was associated with ≥10% lower risk of sensory (RR: 0.90, 0.86-0.95), circulatory (RR: 0.88, 0.83-0.94), blood and hematologic (RR: 0.79, 0.71-0.89), skin and subcutaneous (RR: 0.69, 0.66-0.72), and non-specific COVID-19 related disorders (RR: 0.53, 0.51-0.56). In general, associations were stronger at younger ages but mostly persisted regardless of SARS-CoV-2 variant period, receipt of ≥3 vs. 1-2 vaccine doses, or time since vaccination. Pre-infection vaccination was associated with reduced risk of several PCC outcomes and hence may decrease the long-term consequences of COVID-19.

Three-dimensional (3D) printing with polycarbonate (PC) plastic occurs in manufacturing settings, homes, and schools. Emissions generated during printing with PC stock and bisphenol-A (BPA), an endocrine disrupter in PC, may induce adverse health effects. Inhalation of 3D printer emissions, and changes in endocrine function may lead to cardiovascular dysfunction. The goal of this study was to determine whether there were any changes in markers of peripheral or cardiovascular dysfunction in animals exposed to PC-emissions. Male Sprague Dawley rats were exposed to PC-emissions generated by 3D printing for 1, 4, 8, 15 or 30 d. Exposure induced a reduction in the expression of the antioxidant catalase (Cat) and endothelial nitric oxide synthase (eNos). Endothelin and hypoxia-induced factor 1α transcripts increased after 30 d. Alterations in transcription were associated with elevations in immunostaining for estrogen and androgen receptors, nitrotyrosine, and vascular endothelial growth factor in cardiac arteries of PC-emission exposed animals. There was also a reduction eNOS immunostaining in cardiac arteries from rats exposed to PC-emissions. Histological analyses of heart sections revealed that exposure to PC-emissions resulted in vasoconstriction of cardiac arteries and thickening of the vascular smooth muscle wall, suggesting there was a prolonged vasoconstriction. These findings are consistent with studies showing that inhalation 3D-printer emissions affect cardiovascular function. Although BPA levels in animals were relatively low, exposure-induced changes in immunostaining for estrogen and androgen receptors in cardiac arteries suggest that changes in the action of steroid hormones may have contributed to the alterations in morphology and markers of cardiac function.

During fused filament fabrication (FFF) 3D printing with polycarbonate (PC) filament, a release of ultrafine particles (UFPs) and volatile organic compounds (VOCs) occurs. This study aimed to determine PC filament printing emission-induced toxicity in rats via whole-body inhalation exposure. Male Sprague Dawley rats were exposed to a single concentration (0.529 mg/m(3), 40 nm mean diameter) of the 3D PC filament emissions in a time-course via whole body inhalation for 1, 4, 8, 15, and 30 days (4 hr/day, 4 days/week), and sacrificed 24 hr after the last exposure. Following exposures, rats were assessed for pulmonary and systemic responses. To determine pulmonary injury, total protein and lactate dehydrogenase (LDH) activity, surfactant proteins A and D, total as well as lavage fluid differential cells in bronchoalveolar lavage fluid (BALF) were examined, as well as histopathological analysis of lung and nasal passages was performed. To determine systemic injury, hematological differentials, and blood biomarkers of muscle, metabolic, renal, and hepatic functions were also measured. Results showed that inhalation exposure induced no marked pulmonary or systemic toxicity in rats. In conclusion, inhalation exposure of rats to a low concentration of PC filament emissions produced no significant pulmonary or systemic toxicity.

BACKGROUND: Understanding the long-term impact of the COVID-19 pandemic on health care utilization is important to health care organizations and policy makers for strategic planning, as well as to researchers when designing studies that use observational electronic health record data during the pandemic period. OBJECTIVE: To evaluate changes in health care utilization across all care settings among a large diverse insured population in the United States during the COVID-19 pandemic. METHODS: We conducted a retrospective cohort study within 8 health care organizations participating in the Vaccine Safety Datalink Project using electronic health record data from members of all ages during January 1, 2017 - December 31, 2021. The visit rates per person-year were calculated monthly during the study period for four health care settings combined as well as by inpatient, emergency department (ED), outpatient, and telehealth settings, both among all members and members without COVID-19. Difference-in-difference analysis and interrupted time series analysis were performed to assess the changes in visit rates from the pre-pandemic period (January 2017 to February 2020) to the early pandemic period (April 2020 to December 2020) and the later pandemic period (July 2021 to December 2021), respectively. An exploratory analysis was also conducted to assess trends through June 2023 at one of the largest sites, Kaiser Permanente Southern California (KPSC). RESULTS: The study included more than 11 million members from 2017 to 2021. Compared with the pre-pandemic period, we found reductions in visit rates during the early pandemic period for all in-person care settings. During the later pandemic period, overall utilization reached 8.36 visits per person-year, exceeding the pre-pandemic level of 7.49 visits per person-year in 2019 (adjusted percent change = 5.1%; 95% CI 0.6% to 9.9%); inpatient and ED visits returned to pre-pandemic levels, 0.103 and 0.275 visits per person-year respectively among all members, although they remained 7.5% and 8.0% lower than pre-pandemic levels among members without a documented history of COVID-19. Telehealth visits, which were approximately 42% of the volume of outpatient visits during the later pandemic period, were increased by 97.5% (95% CI 86.0% to 109.7%) from 0.865 visits per person-year in 2019 to 2.35 visits per person-year in the later pandemic period. The trends in KPSC were like those of the entire study population. Visit rates from January 2022 to June 2023 were stable and appeared to be a continuation of the utilization levels observed at the end of 2021. CONCLUSIONS: Telehealth services became a mainstay of the health care system during the late COVID-19 pandemic period. Inpatient and ED visits returned to pre-pandemic levels, although they remained low among members without evidence of COVID-19. Our findings provide valuable information for longer-term strategic resource allocation for patient care in the post-pandemic period and for designing observational studies involving the pandemic period.

Breast cancer treatment can be improved with biomarkers for early detection and individualized therapy. A set of 86 microRNAs (miRNAs) were identified to separate breast cancer tumors from normal breast tissues (n = 52) with an overall accuracy of 90.4%. Six miRNAs had concordant expression in both tumors and breast cancer patient blood samples compared with the normal control samples. Twelve miRNAs showed concordant expression in tumors vs. normal breast tissues and patient survival (n = 1093), with seven as potential tumor suppressors and five as potential oncomiRs. From experimentally validated target genes of these 86 miRNAs, pan-sensitive and pan-resistant genes with concordant mRNA and protein expression associated with in-vitro drug response to 19 NCCN-recommended breast cancer drugs were selected. Combined with in-vitro proliferation assays using CRISPR-Cas9/RNAi and patient survival analysis, MEK inhibitors PD19830 and BRD-K12244279, pilocarpine, and tremorine were discovered as potential new drug options for treating breast cancer. Multi-omics biomarkers of response to the discovered drugs were identified using human breast cancer cell lines. This study presented an artificial intelligence pipeline of miRNA-based discovery of biomarkers, therapeutic targets, and repositioning drugs that can be applied to many cancer types.

Background Shoulder injury related to vaccine administration (SIRVA) accounts for more than half of all claims received by the National Vaccine Injury Compensation Program. However, there is a lack of population-based studies due to the challenge of identifying SIRVA cases in large health care databases.Objective To develop a natural language processing (NLP) method to identify SIRVA cases from clinical notes.Methods We conducted the study among members of a large integrated health care organization who were vaccinated between 04/1/2016 and 12/31/2017 and had subsequent diagnosis codes indicative of shoulder injury. Based on a training dataset with a chart review reference standard of 164 individuals, we developed an NLP algorithm to extract shoulder disorder information, including prior vaccination, anatomic location, temporality and causality. The algorithm identified three groups of positive SIRVA cases (definite, probable and possible) based on the strength of evidence. We compared NLP results to a chart review reference standard of 100 vaccinated individuals. We then applied the final automated NLP algorithm to a broader cohort of vaccinated individuals with a shoulder injury diagnosis code and performed manual chart confirmation on a random sample of NLP-identified definite cases and all NLP-identified probable and possible cases.Results In the validation sample, the NLP algorithm had 100% accuracy for identifying 4 SIRVA cases and 96 individuals without SIRVA. In the broader cohort of 53,585 individuals, the NLP algorithm identified 291 definite, 124 probable, and 52 possible SIRVA cases. The chart-confirmation rates for these groups were 95.3%, 67.7% and 18.9%, respectively.Conclusions The algorithm performed with high sensitivity and reasonable specificity in identifying positive SIRVA cases. The NLP algorithm can potentially be used in future population-based studies to identify this rare adverse event, avoiding labor-intensive chart review validation.Competing Interest StatementLina Sy received research support from GlaxoSmithKline, Dynavax, Seqirus, and Novavax for studies unrelated to this paper. All other authors report no conflicts of interest related to the submitted work.Funding StatementThis study was funded through the Vaccine Safety Datalink under contract 200-2012-53580 from the Centers for Disease Control and Prevention (CDC). The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:The Institutional Review Board at KPSC approved this study.All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesThe datasets generated and/or analyzed during the current study are not publicly available due to ethical standards. The authors do not have permission to share data.CIconfidence intervalEHRelectronic health recordsICD-10-CMInternational Classification of Diseases 10th Revision Clinical ModificationKPSCKaiser Permanente Southern CaliforniaNLPnatural language processingNPVnegative predictive valuePPVpos tive predictive valueSIRVAshoulder injury related to vaccine administrationVICPNational Vaccine Injury Compensation Program

There is currently no gene expression assay that can assess if premalignant lesions will develop into invasive breast cancer. This study sought to identify biomarkers for selecting patients with a high potential for developing invasive carcinoma in the breast with normal histology, benign lesions, or premalignant lesions. A set of 26-gene mRNA expression profiles were used to identify invasive ductal carcinomas from histologically normal tissue and benign lesions and to select those with a higher potential for future cancer development (ADHC) in the breast associated with atypical ductal hyperplasia (ADH). The expression-defined model achieved an overall accuracy of 94.05% (AUC = 0.96) in classifying invasive ductal carcinomas from histologically normal tissue and benign lesions (n = 185). This gene signature classified cancer development in ADH tissues with an overall accuracy of 100% (n = 8). The mRNA expression patterns of these 26 genes were validated using RT-PCR analyses of independent tissue samples (n = 77) and blood samples (n = 48). The protein expression of PBX2 and RAD52 assessed with immunohistochemistry were prognostic of breast cancer survival outcomes. This signature provided significant prognostic stratification in The Cancer Genome Atlas breast cancer patients (n = 1100), as well as basal-like and luminal A subtypes, and was associated with distinct immune infiltration and activities. The mRNA and protein expression of the 26 genes was associated with sensitivity or resistance to 18 NCCN-recommended drugs for treating breast cancer. Eleven genes had significant proliferative potential in CRISPR-Cas9/RNAi screening. Based on this gene expression signature, the VEGFR inhibitor ZM-306416 was discovered as a new drug for treating breast cancer.

BACKGROUND: Traditional active vaccine safety monitoring involves pre-specifying health outcomes and biologically plausible outcome-specific time windows of concern, limiting the adverse events that can be evaluated. In this study, we used tree-based scan statistics to look broadly for >60,000 possible adverse events after bivalent COVID-19 vaccination. METHODS: Vaccine Safety Datalink enrollees aged ≥5 years receiving Moderna or Pfizer-BioNTech bivalent COVID-19 vaccine through November 2022 were followed for 56 days post-vaccination. Incident diagnoses in inpatient or emergency department settings were analyzed for clustering within the hierarchical ICD-10-CM diagnosis code "tree" and temporally within post-vaccination follow-up. The conditional self-controlled tree-temporal scan statistic was used, conditioning on total number of cases of each diagnosis and total number of cases of any diagnosis occurring during the scanning risk window across the entire tree. P = 0.01 was the pre-specified cut-off for statistical significance. RESULTS: Analysis included 352,509 doses of Moderna and 979,189 doses of Pfizer-BioNTech bivalent vaccines. After Moderna vaccination, no statistically significant clusters were found. After Pfizer-BioNTech, there were clusters of unspecified adverse events (Days 1-3, p = 0.0001-0.0007), influenza (Days 35-56, p = 0.0001), cough (Days 44-55, p = 0.0002), and COVID-19 (Days 52-56, p = 0.0004). CONCLUSIONS: For Pfizer-BioNTech only, we detected clusters of: (1) unspecified adverse effects, as have been observed in other vaccine studies using this method, and (2) respiratory disease toward the end of follow-up. The respiratory clusters were likely due to overlap of follow-up with the spread of respiratory syncytial virus, influenza, and COVID-19, i.e., confounding by seasonality. The untargeted nature of the method and its inherent adjustment for the many diagnoses and risk intervals evaluated are unique advantages. Limitations include susceptibility to time-varying confounding, lower statistical power for assessing risks of specific outcomes than in traditional studies targeting fewer outcomes, and the possibility of missing adverse events not strongly clustered in time or within the "tree."

Three-dimensional (3D) printing of manufactured goods has increased in the last 10 years. The increased use of this technology has resulted in questions regarding the influence of inhaling emissions generated during printing. The goal of this study was to determine if inhalation of particulate and/or toxic chemicals generated during printing with polycarbonate (PC) plastic affected the neuroendocrine system. Male rats were exposed to 3D-printer emissions (592 µg particulate/m(3) air) or filtered air for 4 h/day (d), 4 days/week and total exposures lengths were 1, 4, 8, 15 or 30 days. The effects of these exposures on hormone concentrations, and markers of function and/or injury in the olfactory bulb, hypothalamus and testes were measured after 1, 8 and 30 days exposure. Thirty days of exposure to 3D printer emissions resulted in reductions in thyroid stimulating hormone, follicle stimulating hormone and prolactin. These changes were accompanied by (1) elevation in markers of cell injury; (2) reductions in active mitochondria in the olfactory bulb, diminished gonadotropin releasing hormone cells and fibers as well as less tyrosine hydroxylase immunolabeled fibers in the arcuate nucleus; and (3) decrease in spermatogonium. Polycarbonate plastics may contain bisphenol A, and the effects of exposure to these 3D printer-generated emissions on neuroendocrine function are similar to those noted following exposure to bisphenol A.

Laboratory tests were conducted to characterize the composition of emissions from sanding Corian(®), a solid-surface composite material mainly composed of alumina trihydrate (ATH) and acrylic polymer. Three sandpaper materials (ceramic, silicon carbide, and aluminum oxide) were tested to distinguish the contribution of aluminum-containing dust in the emission from Corian(®) and sandpaper itself. The result can help identify the main cause of the pulmonary fibrosis from exposure to aluminum-containing dust while sanding Corian(®). Airborne dust samples were measured using direct-reading instruments and collected using a Micro-Orifice Uniform Deposit Impactor (MOUDI) for estimating the normalized dust generation rate. The size-classified dust samples from MOUDI were analyzed for elemental aluminum content. Additionally, air samples were analyzed for characterizing methyl methacrylate (MMA). The results from the direct-reading instruments reveal that the size distribution of particulate from sanding Corian(®) differs from that of sawing Corian(®), showing that the size distribution of dust is affected by the fabrication process. The normalized respirable dust generation rate indicates that more respirable dust was generated during sanding Corian(®) board. However, the use of aluminum oxide sandpaper does not result in a higher aluminum content in the respirable dust from sanding Corian(®), suggesting that the aluminum content of the respirable dust is primarily originated from Corian(®) itself. The generation rates of MMA from sanding did not vary much among all types of sandpapers, and they were much lower than that of sawing, likely due to the higher temperature in the sawing process.

BACKGROUND: The World Health Organization recommends drug-susceptibility testing of Mycobacterium tuberculosis complex for all patients with tuberculosis to guide treatment decisions and improve outcomes. Whether DNA sequencing can be used to accurately predict profiles of susceptibility to first-line antituberculosis drugs has not been clear. METHODS: We obtained whole-genome sequences and associated phenotypes of resistance or susceptibility to the first-line antituberculosis drugs isoniazid, rifampin, ethambutol, and pyrazinamide for isolates from 16 countries across six continents. For each isolate, mutations associated with drug resistance and drug susceptibility were identified across nine genes, and individual phenotypes were predicted unless mutations of unknown association were also present. To identify how whole-genome sequencing might direct first-line drug therapy, complete susceptibility profiles were predicted. These profiles were predicted to be susceptible to all four drugs (i.e., pansusceptible) if they were predicted to be susceptible to isoniazid and to the other drugs or if they contained mutations of unknown association in genes that affect susceptibility to the other drugs. We simulated the way in which the negative predictive value changed with the prevalence of drug resistance. RESULTS: A total of 10,209 isolates were analyzed. The largest proportion of phenotypes was predicted for rifampin (9660 [95.4%] of 10,130) and the smallest was predicted for ethambutol (8794 [89.8%] of 9794). Resistance to isoniazid, rifampin, ethambutol, and pyrazinamide was correctly predicted with 97.1%, 97.5%, 94.6%, and 91.3% sensitivity, respectively, and susceptibility to these drugs was correctly predicted with 99.0%, 98.8%, 93.6%, and 96.8% specificity. Of the 7516 isolates with complete phenotypic drug-susceptibility profiles, 5865 (78.0%) had complete genotypic predictions, among which 5250 profiles (89.5%) were correctly predicted. Among the 4037 phenotypic profiles that were predicted to be pansusceptible, 3952 (97.9%) were correctly predicted. CONCLUSIONS: Genotypic predictions of the susceptibility of M. tuberculosis to first-line drugs were found to be correlated with phenotypic susceptibility to these drugs. (Funded by the Bill and Melinda Gates Foundation and others.).

Simple Summary: This study identified a set of 73 microRNAs (miRNAs) that can accurately detect lung cancer tumors from normal lung tissues. Based on the consistent expression patterns associated with patient survival outcomes and in tumors vs. normal lung tissues, 10 miRNAs were considered to be putatively tumor suppressive and 4 miRNAs were deemed as oncogenic in lung cancer. From the list of genes that were targeted by the 73 diagnostic miRNAs, DGKE and WDR47 had significant associations with responses to both systemic therapies and radiotherapy in lung cancer. Based on our identified miRNA-regulated network, we discovered three drugs—BX-912, daunorubicin, and midostaurin—that can be repositioned to treat lung cancer, which was not known before. The majority of lung cancer patients are diagnosed with metastatic disease. This study identified a set of 73 microRNAs (miRNAs) that classified lung cancer tumors from normal lung tissues with an overall accuracy of 96.3% in the training patient cohort (n = 109) and 91.7% in unsupervised classification and 92.3% in supervised classification in the validation set (n = 375). Based on association with patient survival (n = 1016), 10 miRNAs were identified as potential tumor suppressors (hsa-miR-144, hsa-miR-195, hsa-miR-223, hsa-miR-30a, hsa-miR-30b, hsa-miR-30d, hsa-miR-335, hsa-miR-363, hsa-miR-451, and hsa-miR-99a), and 4 were identified as potential oncogenes (hsa-miR-21, hsa-miR-31, hsa-miR-411, and hsa-miR-494) in lung cancer. Experimentally confirmed target genes were identified for the 73 diagnostic miRNAs, from which proliferation genes were selected from CRISPR-Cas9/RNA interference (RNAi) screening assays. Pansensitive and panresistant genes to 21 NCCN-recommended drugs with concordant mRNA and protein expression were identified. DGKE and WDR47 were found with significant associations with responses to both systemic therapies and radiotherapy in lung cancer. Based on our identified miRNA-regulated molecular machinery, an inhibitor of PDK1/Akt BX-912, an anthracycline antibiotic daunorubicin, and a multi-targeted protein kinase inhibitor midostaurin were discovered as potential repositioning drugs for treating lung cancer. These findings have implications for improving lung cancer diagnosis, optimizing treatment selection, and discovering new drug options for better patient outcomes.

BACKGROUND: The safety of COVID-19 vaccines plays an important role in addressing vaccine hesitancy. We conducted a large cohort study to evaluate the risk of non-COVID-19 mortality after COVID-19 vaccination while adjusting for confounders including individual-level demographics, clinical risk factors, health care utilization, and community-level socioeconomic risk factors. METHODS: The retrospective cohort study consisted of members from seven Vaccine Safety Datalink sites from December 14, 2020 through August 31, 2021. We conducted three separate analyses for each of the three COVID-19 vaccines used in the US. Crude non-COVID-19 mortality rates were reported by vaccine type, age, sex, and race/ethnicity. The counting process model for survival analyses was used to analyze non-COVID-19 mortality where a new observation period began when the vaccination status changed upon receipt of the first dose and the second dose. We used calendar time as the basic time scale in survival analyses to implicitly adjust for season and other temporal trend factors. A propensity score approach was used to adjust for the potential imbalance in confounders between the vaccinated and comparison groups. RESULTS: For each vaccine type and across age, sex, and race/ethnicity groups, crude non-COVID-19 mortality rates among COVID-19 vaccinees were lower than those among comparators. After adjusting for confounders with the propensity score approach, the adjusted hazard ratios (aHRs) were 0.46 (95% confidence interval [CI], 0.44-0.49) after dose 1 and 0.48 (95% CI, 0.46-0.50) after dose 2 of the BNT162b2 vaccine, 0.41 (95% CI, 0.39-0.44) after dose 1 and 0.38 (95% CI, 0.37-0.40) after dose 2 of the mRNA-1273 vaccine, and 0.55 (95% CI, 0.51-0.59) after receipt of Ad26.COV2.S. CONCLUSION: While residual confounding bias remained after adjusting for several individual-level and community-level risk factors, no increased risk was found for non-COVID-19 mortality among recipients of three COVID-19 vaccines used in the US.

BACKGROUND: Except for spontaneous reporting systems, vaccine safety monitoring generally involves pre-specifying health outcomes and post-vaccination risk windows of concern. Instead, we used tree-based data-mining to look more broadly for possible adverse events after Pfizer-BioNTech, Moderna, and Janssen COVID-19 vaccination. METHODS: Vaccine Safety Datalink enrollees receiving1 dose of COVID-19 vaccine in 2020-2021 were followed for 70days after Pfizer-BioNTech or Moderna and 56days after Janssen vaccination. Incident diagnoses in inpatient or emergency department settings were analyzed for clustering within both the hierarchical ICD-10-CM code structure and the post-vaccination follow-up period. We used the self-controlled tree-temporal scan statistic and TreeScan software. Monte Carlo simulation was used to estimate p-values; p=0.01 was the pre-specified cut-off for statistical significance of a cluster. RESULTS: There were 4.1, 2.6, and 0.4 million Pfizer-BioNTech, Moderna, and Janssen vaccinees, respectively. Clusters after Pfizer-BioNTech vaccination included: (1) unspecified adverse effects, (2) common vaccine reactions, such as fever, myalgia, and headache, (3) myocarditis/pericarditis, and (4) less specific cardiac or respiratory symptoms, all with the strongest clusters generally after Dose 2; and (5) COVID-19/viral pneumonia/sepsis/respiratory failure in the first 3weeks after Dose 1. Moderna results were similar but without a significant myocarditis/pericarditis cluster. Further investigation suggested the fifth signal group was a manifestation of mRNA vaccine effectiveness after the first 3weeks. Janssen vaccinees had clusters of unspecified or common vaccine reactions, gait/mobility abnormalities, and muscle weakness. The latter two were deemed to have arisen from confounding related to practices at one site. CONCLUSIONS: We detected post-vaccination clusters of unspecified adverse effects, common vaccine reactions, and, for the mRNA vaccines, chest pain and palpitations, as well as myocarditis/pericarditis after Pfizer-BioNTech Dose 2. Unique advantages of this data mining are its untargeted nature and its inherent adjustment for the multiplicity of diagnoses and risk intervals scanned.

BACKGROUND: The Centers for Disease Control and Prevention's Vaccine Safety Datalink (VSD) has been performing safety surveillance for COVID-19 vaccines since their earliest authorization in the United States. Complementing its real-time surveillance for pre-specified health outcomes using pre-specified risk intervals, the VSD conducts tree-based data-mining to look for clustering of a broad range of health outcomes after COVID-19 vaccination. This study's objective was to use this untargeted, hypothesis-generating approach to assess the safety of first booster doses of Pfizer-BioNTech (BNT162b2), Moderna (mRNA-1273), and Janssen (Ad26.COV2.S) COVID-19 vaccines. METHODS: VSD enrollees receiving a first booster of COVID-19 vaccine through April 2, 2022 were followed for 56 days. Incident diagnoses in inpatient or emergency department settings were analyzed for clustering within both the hierarchical ICD-10-CM code structure and the follow-up period. The self-controlled tree-temporal scan statistic was used, conditioning on the total number of cases for each diagnosis. P-values were estimated by Monte Carlo simulation; p = 0.01 was pre-specified as the cut-off for statistical significance of clusters. RESULTS: More than 2.4 and 1.8 million subjects received Pfizer-BioNTech and Moderna boosters after an mRNA primary series, respectively. Clusters of urticaria/allergy/rash were found during Days 10-15 after the Moderna booster (p = 0.0001). Other outcomes that clustered after mRNA boosters, mostly with p = 0.0001, included unspecified adverse effects, common vaccine-associated reactions like fever and myalgia, and COVID-19. COVID-19 clusters were in Days 1-10 after booster receipt, before boosters would have become effective. There were no noteworthy clusters after boosters following primary Janssen vaccination. CONCLUSIONS: In this untargeted data-mining study of COVID-19 booster vaccination, a cluster of delayed-onset urticaria/allergy/rash was detected after the Moderna booster, as has been reported after Moderna vaccination previously. Other clusters after mRNA boosters were of unspecified or common adverse effects and COVID-19, the latter evidently reflecting immunity to COVID-19 after 10 days.

BACKGROUND: Natural language processing (NLP) of unstructured text from Electronic Medical Records (EMR) can improve characterization of COVID-19 signs and symptoms, but large-scale studies demonstrating the real-world application and validation of NLP for this purpose are limited. OBJECTIVE: To assess the contribution of NLP when identifying COVID-19 signs and symptoms from EMR. METHODS: This study was conducted in Kaiser Permanente Southern California, a large integrated healthcare system using data from all patients with positive SARS-CoV-2 laboratory tests from March 2020 to May 2021. An NLP algorithm was developed to extract free text from EMR on 12 established signs and symptoms of COVID-19, including fever, cough, headache, fatigue, dyspnea, chills, sore throat, myalgia, anosmia, diarrhea, vomiting/nausea and abdominal pain. The proportion of patients reporting each symptom and the corresponding onset dates were described before and after supplementing structured EMR data with NLP-extracted signs and symptoms. A random sample of 100 chart-reviewed and adjudicated SARS-CoV-2 positive cases were used to validate the algorithm performance. RESULTS: A total of 359,938 patients (mean age: 40.4 years; 53% female) with confirmed SARS-CoV-2 infection were identified over the study period. The most common signs and symptoms identified through NLP-supplemented analyses were cough (61%), fever (52%), myalgia (43%), and headache (40%). The NLP algorithm identified an additional 55,568 (15%) symptomatic cases that were previously defined as asymptomatic using structured data alone. The proportion of additional cases with each selected symptom identified in NLP-supplemented analysis varied across the selected symptoms, from 29% of all records for cough, to 61% of all records with nausea or vomiting. Of 295,305 symptomatic patients, the median time from symptom onset to testing was 3 days using structured data alone, whereas the NLP-algorithm identified signs or symptoms approximately one day earlier. When validated against chart-reviewed cases, the NLP algorithm successfully identified most signs and symptoms with consistently high sensitivity (ranging from 87% to 100%) and specificity (94% to 100%). CONCLUSIONS: These findings demonstrate that NLP can identify and characterize a broad set of COVID-19 signs and symptoms from unstructured data within the EMR, with enhanced detail and timeliness compared with structured data alone.

BACKGROUND: Studying the safety of travel vaccines poses challenges since recipients may be traveling during the risk window for adverse events and the identification of a suitable comparison group can also be difficult. The examination of traveler characteristics, travel vaccination patterns, and health care utilization using electronic health record (EHR) data can inform the feasibility of future travel vaccine safety studies. METHODS: A retrospective cohort study of health plan members in the Vaccine Safety Datalink Project aged 9 months and older who had a travel-related encounter or received a travel vaccine from 2009 to 2018 was performed. Travel regions visited, travel duration, type of travel vaccine received (typhoid, yellow fever, Japanese encephalitis, rabies, and cholera), and timing of vaccination date before departure date were described. Sociodemographic information, clinical characteristics, and health care utilization were compared between travelers who received travel vaccines and travelers who did not. RESULTS: A total of 1,026,822 unique travelers departing from the United States were identified; 612,795 travelers received 898,196 doses of travel vaccines. The most commonly administered travel vaccine was typhoid vaccine and 77% of all travel vaccines were given more than one week prior to departure. Compared with travelers without travel vaccines, travelers with travel vaccines were overall similar but as a group were slightly younger, healthier, and had lower Hispanic representation. Health care utilization dramatically decreased during travel. Outpatient visits decreased from 294.8 visits per 10,000 person-days before travel to 24.2 visits per 10,000 person-days during reported travel dates. CONCLUSIONS: Through the EHR information from almost a million travelers, a departure date and duration of travel were successfully captured for the majority of travelers with corresponding health care utilization data. Time after vaccination and prior to departure can potentially be used in the future to compare travelers who receive travel vaccines with travelers who do not receive travel vaccines when looking at adverse events of interest after vaccination.

IMPORTANCE: After SARS-CoV-2 infection, many patients present with persistent symptoms for at least 6 months, collectively termed post-COVID conditions (PCC). However, the impact of PCC on health care utilization has not been well described. OBJECTIVES: To estimate COVID-19-associated excess health care utilization following acute SARS-CoV-2 infection and describe utilization for select PCCs among patients who had positive SARS-CoV-2 test results (including reverse transcription-polymerase chain reaction and antigen tests) compared with control patients whose results were negative. DESIGN, SETTING, AND PARTICIPANTS: This matched retrospective cohort study included patients of all ages from 8 large integrated health care systems across the United States who completed a SARS-CoV-2 diagnostic test during March 1 to November 1, 2020. Patients were matched on age, sex, race and ethnicity, site, and date of SARS-CoV-2 test and were followed-up for 6 months. Data were analyzed from March 18, 2021, to June 8, 2022. EXPOSURE: SARS-CoV-2 infection. MAIN OUTCOMES AND MEASURES: Ratios of rate ratios (RRRs) for COVID-19-associated health care utilization were calculated with a difference-in-difference analysis using Poisson regression models. RRRs were estimated overall, by health care setting, by select population characteristics, and by 44 PCCs. COVID-19-associated excess health care utilization was estimated by health care setting. RESULTS: The final matched cohort included 127 859 patients with test results positive for SARS-CoV-2 and 127 859 patients with test results negative for SARS-CoV-2. The mean (SD) age of the study population was 41.2 (18.6) years, 68 696 patients in each group (53.7%) were female, and each group included 66 211 Hispanic patients (51.8%), 9122 non-Hispanic Asian patients (7.1%), 7983 non-Hispanic Black patients (6.2%), and 34 326 non-Hispanic White patients (26.9%). Overall, SARS-CoV-2 infection was associated with a 4% increase in health care utilization over 6 months (RRR, 1.04 [95% CI, 1.03-1.05]), predominantly for virtual encounters (RRR, 1.14 [95% CI, 1.12-1.16]), followed by emergency department visits (RRR, 1.08 [95% CI, 1.04-1.12]). COVID-19-associated utilization for 18 PCCs remained elevated 6 months from the acute stage of infection, with the largest increase in COVID-19-associated utilization observed for infectious disease sequelae (RRR, 86.00 [95% CI, 5.07-1458.33]), COVID-19 (RRR, 19.47 [95% CI, 10.47-36.22]), alopecia (RRR, 2.52 [95% CI, 2.17-2.92]), bronchitis (RRR, 1.85 [95% CI, 1.62-2.12]), pulmonary embolism or deep vein thrombosis (RRR, 1.74 [95% CI, 1.36-2.23]), and dyspnea (RRR, 1.73 [95% CI, 1.61-1.86]). In total, COVID-19-associated excess health care utilization amounted to an estimated 27 217 additional medical encounters over 6 months (212.9 [95% CI, 146.5-278.4] visits per 1000 patients). CONCLUSIONS AND RELEVANCE: This cohort study documented an excess health care burden of PCC in the 6 months after the acute stage of infection. As health care systems evolve during a highly dynamic and ongoing global pandemic, these data provide valuable evidence to inform long-term strategic resource allocation for patients previously infected with SARS-CoV-2.

Over the past two years, scientific research has moved at an unprecedented rate in response to the COVID-19 pandemic. The rapid development of effective vaccines and therapeutics would not have been possible without extensive background knowledge on coronaviruses developed over decades by researchers, including Kathryn (Kay) Holmes. Kay's research team discovered the first coronavirus receptors for mouse hepatitis virus and human coronavirus 229E and contributed a wealth of information on coronaviral spike glycoproteins and receptor interactions that are critical determinants of host and tissue specificity. She collaborated with several research laboratories to contribute knowledge in additional areas, including coronaviral pathogenesis, epidemiology, and evolution. Throughout her career, Kay was an extremely dedicated and thoughtful mentor to numerous graduate students and post-doctoral fellows. This article provides a review of her contributions to the coronavirus field and her exemplary mentoring.

Artificial countertop materials, including solid surface composites (SSC) and engineered stone (ES) may pose significant pulmonary health risks for workers who manipulate them. These materials have rapidly become popular in the multibillion-dollar countertop industry, rivaling that of natural materials such as granite and marble due to their variety of desirable esthetic qualities and reduced costs. Both SSC and ES consist of a mineral substrate bound together in a polymer matrix. For SSC the mineral is about 70% aluminum trihydrate (ATH) while ES contains up to 95% crystalline silica by weight. Both materials emit airborne dusts when being manipulated with power tools during the fabrication process. Several deaths and dozens of cases of silicosis have been identified worldwide in workers who fabricate ES, while a single case of fatal pulmonary fibrosis has been associated with SCC dust exposure. This review examines the current state of knowledge for both SSC and ES regarding the composition, particle emission characteristics, workplace exposure data, particle constituent toxicity, and possible methods for reducing worker exposure.

This study investigated the inhalation toxicity of the emissions from 3-D printing with acrylonitrile butadiene styrene (ABS) filament using an air-liquid interface (ALI) in vitro model. Primary normal human-derived bronchial epithelial cells (NHBEs) were exposed to ABS filament emissions in an ALI for 4 hours. The mean and mode diameters of ABS emitted particles in the medium were 175 ± 24 and 153 ± 15 nm, respectively. The average particle deposition per surface area of the epithelium was 2.29 × 10(7) ± 1.47 × 10(7) particle/cm(2), equivalent to an estimated average particle mass of 0.144 ± 0.042 μg/cm(2). Results showed exposure of NHBEs to ABS emissions did not significantly affect epithelium integrity, ciliation, mucus production, nor induce cytotoxicity. At 24 hours after the exposure, significant increases in the pro-inflammatory markers IL-12p70, IL-13, IL-15, IFN-γ, TNF-α, IL-17A, VEGF, MCP-1, and MIP-1α were noted in the basolateral cell culture medium of ABS-exposed cells compared to non-exposed chamber control cells. Results obtained from this study correspond with those from our previous in vivo studies, indicating that the increase in inflammatory mediators occur without associated membrane damage. The combination of the exposure chamber and the ALI-based model is promising for assessing 3-D printer emission-induced toxicity.

BACKGROUND: Shoulder injury related to vaccine administration (SIRVA) accounts for more than half of all claims received by the National Vaccine Injury Compensation Program. However, due to the difficulty of finding SIRVA cases in large health care databases, population-based studies are scarce. OBJECTIVE: The goal of the research was to develop a natural language processing (NLP) method to identify SIRVA cases from clinical notes. METHODS: We conducted the study among members of a large integrated health care organization who were vaccinated between April 1, 2016, and December 31, 2017, and had subsequent diagnosis codes indicative of shoulder injury. Based on a training data set with a chart review reference standard of 164 cases, we developed an NLP algorithm to extract shoulder disorder information, including prior vaccination, anatomic location, temporality and causality. The algorithm identified 3 groups of positive SIRVA cases (definite, probable, and possible) based on the strength of evidence. We compared NLP results to a chart review reference standard of 100 vaccinated cases. We then applied the final automated NLP algorithm to a broader cohort of vaccinated persons with a shoulder injury diagnosis code and performed manual chart confirmation on a random sample of NLP-identified definite cases and all NLP-identified probable and possible cases. RESULTS: In the validation sample, the NLP algorithm had 100% accuracy for identifying 4 SIRVA cases and 96 cases without SIRVA. In the broader cohort of 53,585 vaccinations, the NLP algorithm identified 291 definite, 124 probable, and 52 possible SIRVA cases. The chart-confirmation rates for these groups were 95.5% (278/291), 67.7% (84/124), and 17.3% (9/52), respectively. CONCLUSIONS: The algorithm performed with high sensitivity and reasonable specificity in identifying positive SIRVA cases. The NLP algorithm can potentially be used in future population-based studies to identify this rare adverse event, avoiding labor-intensive chart review validation.

BACKGROUND: The COVID-19 pandemic caused an abrupt drop in in-person health care (inpatient, Emergency Department, outpatient) and an increase in telehealth care, which poses challenges in vaccine safety studies that identify outcomes from in-person encounters. We examined the changes in incidence rates of selected encounter-based outcomes during the COVID-19 pandemic. METHODS: We assembled a cohort of members from 8 Vaccine Safety Datalink sites from January 1, 2017 through December 31, 2020. Using ICD-10 diagnosis codes or laboratory criteria, we identified 21 incident outcomes in traditional in-person settings and all settings. We defined 4 periods in 2020: January-February (pre-pandemic), April-June (early pandemic), July-September (middle pandemic), and October-December (late pandemic). We defined four corresponding periods in each year during 2017-2019. We calculated incidence rates, conducted difference in difference (DiD) analyses, and reported ratios of incidence rate ratios (RRR) to examine changes in rates from pre-pandemic to early, middle, and late pandemic in 2020, after adjusting for changes across similar periods in 2017-2019. RESULTS: Among>10 million members, regardless of setting and after adjusting for changes during 2017-2019, we found that incidence rates of acute disseminated encephalomyelitis, encephalitis/myelitis/encephalomyelitis/meningoencephalitis, and thrombotic thrombocytopenic purpura did not significantly change from the pre-pandemic to early, middle or late pandemic periods (p-values0.05). Incidence rates decreased from the pre-pandemic to early pandemic period during 2020 for acute myocardial infarction, anaphylaxis, appendicitis, Bell's palsy, convulsions/seizures, Guillain-Barr syndrome, immune thrombocytopenia (ITP), narcolepsy/cataplexy, hemorrhagic stroke, ischemic stroke, and venous thromboembolism (p-values<0.05). Incidence rates of Bell's palsy, ITP, and narcolepsy/cataplexy were higher in all settings than in traditional in-person settings during the three pandemic periods (p-values<0.05). CONCLUSION: Rates of some clinical outcomes during the pandemic changed and should not be used as historical background rates in vaccine safety studies. Inclusion of telehealth visits should be considered for vaccine studies involving Bell's palsy, ITP, and narcolepsy/cataplexy.

BACKGROUND: Although shoulder conditions have been reported as an adverse event after intramuscular vaccination in the deltoid muscle, epidemiologic data on shoulder conditions after vaccination are limited. OBJECTIVE: To estimate the risk for shoulder conditions after vaccination and assess possible risk factors. DESIGN: Retrospective cohort study. SETTING: Kaiser Permanente Southern California, a large integrated health care organization. PARTICIPANTS: Kaiser Permanente Southern California members aged 3 years or older who had an intramuscular vaccination administered in the deltoid muscle between 1 April 2016 and 31 December 2017. MEASUREMENTS: A natural language processing (NLP) algorithm was used to identify potential shoulder conditions among vaccinated persons with shoulder disorder diagnosis codes. All NLP-identified cases were manually chart confirmed on the basis of our case definition. The characteristics of vaccinated persons with and without shoulder conditions were compared. RESULTS: Among 3 758 764 administered vaccinations, 371 cases of shoulder condition were identified, with an estimated incidence of 0.99 (95% CI, 0.89 to 1.09) per 10 000 vaccinations. The incidence was 1.22 (CI, 1.10 to 1.35) for the adult (aged ≥18 years) and 0.05 (CI, 0.02 to 0.14) for the pediatric (aged 3 to 17 years) vaccinated populations. In the adult vaccinated population, advanced age, female sex, an increased number of outpatient visits in the 6 months before vaccination, lower Charlson Comorbidity Index, and pneumococcal conjugate vaccine were associated with a higher risk for shoulder conditions. Among influenza vaccines, quadrivalent vaccines were associated with an increased risk for shoulder conditions. Simultaneous administration of vaccines was associated with a higher risk for shoulder conditions among elderly persons. LIMITATION: Generalizability to other health care settings, use of administrative data, and residual confounding. CONCLUSION: These population-based data suggest a small absolute risk for shoulder conditions after vaccination. Given the high burden of shoulder conditions, clinicians should pay attention to any factors that may further increase risks. PRIMARY FUNDING SOURCE: Centers for Disease Control and Prevention.

OBJECTIVES: To determine whether children aged 4-7 years with a diagnosis of autism spectrum disorders (ASD) were at increased risk of fever, febrile seizures, or emergency department (ED) visits following measles- or pertussis-containing vaccines compared with children without ASD. METHODS: The study included children born between 1995-2012, aged 4-7 years at vaccination, and members of six healthcare delivery systems within Vaccine Safety Datalink. We conducted self-controlled risk interval analyses comparing rates of outcomes in risk and control intervals within each group defined by ASD status, and then compared outcome rates between children with and without ASD, in risk and control intervals, by estimating difference-in-differences using logistic regressions. RESULTS: The study included 14,947 children with ASD and 1,650,041 children without ASD. After measles- or pertussis-containing vaccination, there were no differences in association between children with and without ASD for fever (ratio of rate ratio for measles-containing vaccine = 1.07, 95% CI 0.58-1.96; for pertussis-containing vaccine = 1.16, 95% CI 0.63-2.15) or ED visits (ratio of rate ratio for measles-containing vaccine = 1.11, 95% CI 0.80-1.54; for pertussis-containing vaccine = 0.87, 95% CI 0.59-1.28). Febrile seizures were rare. Pertussis-containing vaccines were associated with small increased risk of febrile seizures in children without ASD. CONCLUSION: Children with ASD were not at increased risk for fever or ED visits compared with children without ASD following measles- or pertussis-containing vaccines. These results may provide further reassurance that these vaccines are safe for all children, including those with ASD.