Beyond their substantial protection of individual vaccinees, coronavirus disease 2019 (COVID-19) vaccines might reduce viral load in breakthrough infection and thereby further suppress onward transmission. In this analysis of a real-world dataset of positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) test results after inoculation with the BNT162b2 messenger RNA vaccine, we found that the viral load was substantially reduced for infections occurring 12-37 d after the first dose of vaccine. These reduced viral loads hint at a potentially lower infectiousness, further contributing to vaccine effect on virus spread.The recently authorized BNT162b2 Coronavirus Disease 2019 (COVID-19) messenger RNA (mRNA) vaccine is approximately 95% efficient in preventing polymerase chain reaction (PCR)-confirmed symptomatic disease from 7 d after the second dose and also provides some early protection starting 12 d after the first dose 1,2 . As countries race to vaccinate a substantial portion of their populations in the coming months, it is hoped that the basic reproduction number of the virus will decrease. This effect can be achieved by reducing the number of susceptible people, as well as by reducing viral load and, thereby, viral shedding of post-vaccination infections, which might render them less infectious [3][4][5][6][7] . However, the effect of vaccination on viral load in COVID-19 post-vaccination infections is currently unknown 8 .As of February 11, 2021, Maccabi Healthcare Services (MHS) in Israel has vaccinated more than 1 million of its members as part of a national rapid rollout of the vaccine. MHS member SARS-CoV-2 tests are often carried out in the MHS central laboratory, which offers the opportunity to track post-vaccination infections. In this study, we retrospectively collected and analyzed the quantitative reverse transcription PCR (RT-qPCR) test measurements of three SARS-CoV-2 genes-E, N and RdRp (Allplex 2019-nCoV assay, Seegene)-from positive post-vaccination tests performed at the MHS central laboratory between December 21, 2020, and February 11, 2021 (n = 4,938 patients, study population; Table 1). The study period was characterized by high and steady rates of positive COVID-19 tests (Extended Data Fig. 1), indicating an ongoing epidemic wave.In an analysis of the infection cycle threshold (Ct) over time, we found that the mean viral load substantially decreased 12 d after vaccination with the first vaccine dose, coinciding with the known early onset of vaccine-mediated protection 1 . When we calculated the mean Ct for post-vaccination infections identified on each day

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Vaccinations are considered the major tool to curb the current SARS-CoV-2 pandemic. A randomized placebo-controlled trial of the BNT162b2 vaccine has demonstrated a 95% efficacy in preventing COVID-19 disease. These results are now corroborated with statistical analyses of real-world vaccination rollouts, but resolving vaccine effectiveness across demographic groups is challenging. Here, applying a multivariable logistic regression analysis approach to a large patient-level dataset, including SARS-CoV-2 tests, vaccine inoculations and personalized demographics, we model vaccine effectiveness at daily resolution and its interaction with sex, age and comorbidities. Vaccine effectiveness gradually increased post day 12 of inoculation, then plateaued, around 35 days, reaching 91.2% [CI 88.8%-93.1%] for all infections and 99.3% [CI 95.3%-99.9%] for symptomatic infections. Effectiveness was uniform for men and women yet declined mildly but significantly with age and for patients with specific chronic comorbidities, most notably type 2 diabetes. Quantifying real-world vaccine effectiveness, including both biological and behavioral effects, our analysis provides initial measurement of vaccine effectiveness across demographic groups.

Background Acute kidney injury (AKI) is a serious global public health problem. We aimed to quantify the risk of AKI associated with estimated glomerular filtration rate (eGFR), albuminuria (albumin-creatinine ratio [ACR]), age, sex, and race (African American and Caucasian). Study Design Collaborative meta-analysis. Setting & Population 8 general population cohorts (1,285,049 participants) and 5 chronic kidney disease (CKD) cohorts (79,519 participants). Selection Criteria for Studies Available eGFR, ACR, and ≥50 AKI events. Predictors Age, sex, race, eGFR, urine ACR, and interactions. Outcome Hospitalized with or for AKI, using Cox proportional hazards models to estimate HRs of AKI and random effects meta-analysis to pool results. Results 16,480 (1.3%) general population cohort participants had AKI over a mean follow-up of 4 years; 2,087 (2.6%) CKD participants had AKI over mean follow-up of 1 year. Lower eGFR and higher ACR were strongly associated with AKI. Compared with eGFR 80 ml/min/1.73 m2, the adjusted HR of AKI at eGFR 45 ml/min/1.73 m2 was 3.35 (95% CI, 2.75–4.07). Compared with ACR 5 mg/g, the risk of AKI at ACR 300 mg/g was 2.73 (95% CI, 2.18–3.43). Older age was associated with higher risk of AKI, but this effect was attenuated in lower eGFR or higher ACR. Male sex was associated with higher risk of AKI, with a slight attenuation in lower eGFR but not in higher ACR. African Americans had higher AKI risk at higher levels of eGFR and most levels of ACR. Limitations Only 2 general population cohorts could contribute to analyses by race; AKI identified by diagnostic code. Conclusions Reduced eGFR and increased ACR are consistent, strong risk factors for AKI whereas the associations of AKI with age, sex, and race may be weaker in more advanced stages of CKD.

Background Waning of protection against infection with SARS-CoV-2 conferred by 2 doses of the BNT162b2 vaccine begins shortly after inoculation and becomes substantial within four months. With that, the impact of prior infection on incident SARS-CoV-2 reinfection is unclear. Therefore, we examined the long-term protection of naturally acquired immunity (protection conferred by previous infection) compared to vaccine-induced immunity. Methods A retrospective observational study of 124,500 persons, compared two groups: (1) SARS-CoV-2-naïve individuals who received a two-dose regimen of the BioNTech/Pfizer mRNA BNT162b2 vaccine, and (2) previously infected individuals who have not been vaccinated. Two multivariate logistic regression models were applied, evaluating four SARS-CoV-2-related outcomes - infection, symptomatic disease (COVID-19), hospitalization and death – between June 1 to August 14, 2021, when the Delta variant was dominant in Israel. Results SARS-CoV-2-naïve vaccinees had a 13.06-fold (95% CI, 8.08-21.11) increased risk for breakthrough infection with the Delta variant compared to unvaccinated-previously-infected individuals, when the first event (infection or vaccination) occurred during January and February of 2021. The increased risk was significant for symptomatic disease as well. When allowing the infection to occur at any time between March 2020 to February 2021, evidence of waning naturally acquired immunity was demonstrated, though SARS-CoV-2 naïve vaccinees still had a 5.96-fold (95% CI, 4.85-7.33) increased risk for breakthrough infection and a 7.13-fold (95% CI, 5.51-9.21) increased risk for symptomatic disease. Conclusions Naturally acquired immunity confers stronger protection against infection and symptomatic disease caused by the Delta variant of SARS-CoV-2, compared to the BNT162b2 two-dose vaccine-induced immunity.

Treatment of bacterial infections currently focuses on choosing an antibiotic that matches a pathogen’s susceptibility, with less attention paid to the risk that even susceptibility-matched treatments can fail as a result of resistance emerging in response to treatment. Combining whole-genome sequencing of 1113 pre- and posttreatment bacterial isolates with machine-learning analysis of 140,349 urinary tract infections and 7365 wound infections, we found that treatment-induced emergence of resistance could be predicted and minimized at the individual-patient level. Emergence of resistance was common and driven not by de novo resistance evolution but by rapid reinfection with a different strain resistant to the prescribed antibiotic. As most infections are seeded from a patient’s own microbiota, these resistance-gaining recurrences can be predicted using the patient’s past infection history and minimized by machine learning–personalized antibiotic recommendations, offering a means to reduce the emergence and spread of resistant pathogens.

reast cancer is the second leading cause of cancer-related deaths and the most commonly diagnosed cancer in women across the world (1). Digital mammography (DM) is the primary imaging modality of breast cancer screening in women who are asymptomatic. In a diagnostic workup setting (2), DM has been shown to reduce breast cancer mortality (3). In standard clinical practice, a radiologist reads mammograms and classifies the findings according to the American College of Radiology (4) Breast Imaging Reporting and Data System (BI-RADS) lexicon. An abnormal finding depicted at DM typically requires a diagnostic workup, which may include additional mammographic views or possibly additional imaging modalities. If a lesion is suspicious for cancer, further evaluation with a biopsy is recommended. Analyzing these images is challenging because of the subtle differences between lesions and background fibroglandular tissue, different lesion types, the nonrigid nature of the breast, and the relatively small proportion of cancers in a screening population of women at average risk (2). This leads to substantial intraobserver and interobserver variability (5). The average performance measures for screening mammography by a radiologist was reported by Lehman et al (6) to be 86.9% sensitivity and 88.9% specificity. Breast cancer risk prediction models on the basis of clinical features can help physicians estimate the probability of an individual or population to develop breast cancer within certain time frames. As a result, they are often used to recommend an individual screening plan. In a systematic survey of risk prediction models, Meads et al (7) reported a limited performance when applied to general populations (area under the receiver operating characteristic curve [AUC], 0.67; 95% confidence interval [CI]: 0.65, 0.68), and showed improved results when applied to high-risk populations (AUC, 0.76; 95% CI: 0.70, 0.82).

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