The World Health Organization framework for tracking SARS-CoV-2 variants has been updated to reflect the continued evolution of the virus; this framework could be adapted for other emerging respiratory diseases with epidemic and pandemic potential.

Abstract Background While numerous studies have evaluated the real-world performance of rapid antigen tests (RATs), data on the effect of Omicron sublineages such as XBB and reinfections on RAT performance is limited. We assessed the performance of RATs and factors associated with RAT-negative results among individuals who tested SARS-CoV-2-positive by reverse transcription-polymerase chain reaction (RT-PCR). Methods We conducted a retrospective study among Singapore residents who underwent testing for SARS-CoV-2 with RAT (Acon Flowflex or SD Biosensor) and RT-PCR in the same clinical encounter between 9 May 2022 and 21 November 2022. RT-PCR served as a reference standard for RAT performance. Logistic regression was used to estimate the odds ratios (OR) of factors associated with negative RAT results among RT-PCR-positive cases. Results Of 8,620 clinical encounters analysed, 3,519 (40.8%) were SARS-CoV-2-positive on RT-PCR. Overall sensitivity and specificity of RAT was 84.6% (95% CI 83.3–85.7%) and 99.4% (95% CI 99.1–99.6%) respectively. Acon Flowflex consistently achieved higher sensitivity and specificity than SD Biosensor test kit. Among RT-PCR-positive cases, individuals who had a previous documented SARS-CoV-2 infection, coinfection with another respiratory pathogen or tested ≥ 6 days from symptom onset had higher odds of testing RAT-negative, but the associations were attenuated after adjustment for cycle threshold values (proxy for viral load). There was no significant difference in RAT performance between Omicron sublineages BA.2, BA.5 and XBB.1. Conclusion Diagnostic performance of RAT was not affected by changes in predominant circulating Omicron sublineages. However, reinfection cases may be under ascertained by RAT. In individuals with a previous SARS-CoV-2 infection episode or symptom onset ≥ 6 days prior to testing, a confirmatory RT-PCR may be considered if there is high clinical suspicion.

Background Longitudinal work on the impact of COVID-19 on population mental health and resilience beyond the first year of the pandemic is lacking. We aimed to understand how mental health and resilience evolved during the pandemic (2020) and two years later (2022) in a multi-ethnic Singaporean population. In addition, we assessed what characteristics were associated with mental health and resilience scores. Methods We surveyed and analysed two balanced panel samples up to four times between 30 th April 2020 and 11 th July 2022. One panel assessed psychological distress (Kessler-10) and well-being (short Warwick Edinburgh Mental Well-being scale) n = 313, and one panel assessed resilience (10-item Connor-Davidson Resilience Scale © ) n = 583. A linear panel regression model with random effects assessed the temporal patterns for psychological distress, well-being, and resilience. Results Mean psychological distress scores (Kessler-10) were relatively stable over time and were not statistically significantly worse than baseline at any follow-up. Well-being scores improved over time and were significantly better than baseline by the third survey (22 nd Jul-18th Aug 2020) (0.54 p = 0.007, Cohen’s d 0.12). Scores had worsened by the last survey (27 th June-11 th July 2022) but were not significantly different from baseline 0.20 p = 0.30. Resilience scores declined over time. Scores at both follow-ups (14th Aug- 4th Sep 2020 and 27 th June-11 th July 2022) were statistically significantly lower than baseline: -1.69 p < 0.001 (Cohen’s d 0.25) and -0.96 p = 0.006 (Cohen’s d 0.14), respectively. Conclusions Our study joins a body of work measuring the longitudinal effects of COVID-19 on population mental health and resilience. While, the magnitude of the effect related to resilience decline is small, our findings indicate that particular attention should be given to ongoing population surveillance, with the aim of maintaining good health and well-being.

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern such as Omicron hampered efforts in controlling the ongoing coronavirus disease 2019 pandemic due to their ability to escape neutralizing antibodies induced by vaccination or prior infection, highlighting the need to develop broad-spectrum vaccines and therapeutics. Most human monoclonal antibodies (mAbs) reported to date have not demonstrated true pan-sarbecovirus neutralizing breadth especially against animal sarbecoviruses. Here, we report the isolation and characterization of highly potent mAbs targeting the receptor binding domain (RBD) of huACE2-dependent sarbecovirus from a SARS-CoV survivor vaccinated with BNT162b2. Among the six mAbs identified, one (E7) showed better huACE2-dependent sarbecovirus neutralizing potency and breadth than any other mAbs reported to date. Mutagenesis and cryo-electron microscopy studies indicate that these mAbs have a unique RBD contact footprint and that E7 binds to a quaternary structure-dependent epitope.

Global sequencing and surveillance capacity for SARS-CoV-2 must be strengthened and combined with multidisciplinary studies of infectivity, virulence and immune escape, in order to track the unpredictable evolution of the ongoing COVID-19 pandemic.

The SARS-CoV-2 B.1.1.529 lineage, Omicron variant, was first detected in November 2021 and carries 32 amino acid mutations in the spike protein (15 in RBD) and exhibits significant escape of neutralizing antibodies targeting the parental SARS-CoV-2 virus. Here, we performed a high-resolution multiplex (16-plex) surrogate virus neutralization assay covering all major SARS-CoV-2 variants and pre-emergent ACE2-binding sarbecoviruses against 20 different human serum panels from infected, vaccinated and hybrid immune individuals which had vaccine-breakthrough infections or infection followed by vaccination. Among all sarbecoviruses tested, we observed 1.1 to 4.7-, 2.3 to 10.3- and 0.7 to 33.3-fold reduction in neutralization activities to SARS-CoV-2 Beta, Omicron and SARS-CoV-1, respectively. Among the SARS-CoV-2 related sarbecoviruses, it is found that the genetically more distant bat RaTG13 and pangolin GX-P5L sarbecoviruses had less neutralization escape than Omicron. Our data suggest that the SARS-CoV-2 variants emerged from the changed immune landscape of human populations are more potent in escaping neutralizing antibodies, from infection or vaccination, than pre-emergent sarbecoviruses naturally evolved in animal populations with no or less immune selection pressure.

We sampled respiratory aerosols emitted by COVID-19 patients and discovered that fine aerosols (<= 5 mu m) generated during talking and singing contain more SARS-CoV-2 copies than coarse aerosols (>5 mu m) and may play a significant role in the transmission of SARS-CoV-2. Background Multiple severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) superspreading events suggest that aerosols play an important role in driving the coronavirus disease 2019 (COVID-19) pandemic. To better understand how airborne SARS-CoV-2 transmission occurs, we sought to determine viral loads within coarse (>5 mu m) and fine (<= 5 mu m) respiratory aerosols produced when breathing, talking, and singing. Methods Using a G-II exhaled breath collector, we measured viral RNA in coarse and fine respiratory aerosols emitted by COVID-19 patients during 30 minutes of breathing, 15 minutes of talking, and 15 minutes of singing. Results Thirteen participants (59%) emitted detectable levels of SARS-CoV-2 RNA in respiratory aerosols, including 3 asymptomatic and 1 presymptomatic patient. Viral loads ranged from 63-5821 N gene copies per expiratory activity per participant, with high person-to-person variation. Patients earlier in illness were more likely to emit detectable RNA. Two participants, sampled on day 3 of illness, accounted for 52% of total viral load. Overall, 94% of SARS-CoV-2 RNA copies were emitted by talking and singing. Interestingly, 7 participants emitted more virus from talking than singing. Overall, fine aerosols constituted 85% of the viral load detected in our study. Virus cultures were negative. Conclusions Fine aerosols produced by talking and singing contain more SARS-CoV-2 copies than coarse aerosols and may play a significant role in SARS-CoV-2 transmission. Exposure to fine aerosols, especially indoors, should be mitigated. Isolating viable SARS-CoV-2 from respiratory aerosol samples remains challenging; whether this can be more easily accomplished for emerging SARS-CoV-2 variants is an urgent enquiry necessitating larger-scale studies.

Background The human intestinal microbiome is a complex community that contributes to host health and disease. In addition to normal microbiota, pathogens like carbapenem-resistant Enterobacteriaceae may be asymptomatically present. When these bacteria are present at very low levels, they are often undetectable in hospital surveillance cultures, known as occult or subclinical colonization. Through the receipt of antibiotics, these subclinical pathogens can increase to sufficiently high levels to become detectable, in a process called outgrowth. However, little is known about the interaction between gut microbiota and Enterobacteriaceae during occult colonization and outgrowth. Results We developed a clinically relevant mouse model for studying occult colonization. Conventional wild-type mice without antibiotic pre-treatment were exposed to Klebsiella pneumoniae but rapidly tested negative for colonization. This occult colonization was found to perturb the microbiome as detected by both 16S rRNA amplicon and shotgun metagenomic sequencing. Outgrowth of occult K. pneumoniae was induced either by a four-antibiotic cocktail or by individual receipt of ampicillin, vancomycin, or azithromycin, which all reduced overall microbial diversity. Notably, vancomycin was shown to trigger K. pneumoniae outgrowth in only a subset of exposed animals (outgrowth-susceptible). To identify factors that underlie outgrowth susceptibility, we analyzed microbiome-encoded gene functions and were able to classify outgrowth-susceptible microbiomes using pathways associated with mRNA stability. Lastly, an evolutionary approach illuminated the importance of xylose metabolism in K. pneumoniae colonization, supporting xylose abundance as a second susceptibility indicator. We showed that our model is generalizable to other pathogens, including carbapenem-resistant Escherichia coli and Enterobacter cloacae . Conclusions Our modeling of occult colonization and outgrowth could help the development of strategies to mitigate the risk of subsequent infection and transmission in medical facilities and the wider community. This study suggests that microbiota mRNA and small-molecule metabolites may be used to predict outgrowth-susceptibility. Video Abstract

OBJECTIVES:WHO recommends that low burden countries consider systematic screening and treatment of latent tuberculosis infection (LTBI) in migrants from high incidence countries. We aimed to determine LTBI prevalence and risk factors and evaluate cost-effectiveness of screening and treating LTBI in migrants to Singapore from a government payer perspective.DESIGN:Cross-sectional study and cost-effectiveness analysis.SETTING:Migrants in Singapore.PARTICIPANTS:3618 migrants who were between 20 and 50 years old, have not worked in Singapore previously and stayed in Singapore for less than a year were recruited.PRIMARY AND SECONDARY OUTCOME MEASURES:Costs, quality-adjusted life-years (QALYs), threshold length of stay, incremental cost-effectiveness ratios (ICERs), cost per active TB case averted.RESULTS:Of 3584 migrants surveyed, 20.4% had positive interferon-gamma release assay (IGRA) results, with the highest positivity in Filipinos (33.2%). Higher LTBI prevalence was significantly associated with age, marital status and past TB exposure. The cost-effectiveness model projected an ICER of S$57 116 per QALY and S$12 422 per active TB case averted for screening and treating LTBI with 3 months once weekly isoniazid and rifapentine combination regimen treatment compared with no screening over a 50-year time horizon. ICER was most sensitive to the cohort's length of stay in Singapore, yearly disease progression rates from LTBI to active TB, followed by the cost of IGRA testing.CONCLUSIONS:For LTBI screening and treatment of migrants to be cost-effective, migrants from high burden countries would have to stay in Singapore for ~50 years. Risk-stratified approaches based on projected length of stay and country of origin and/or age group can be considered.

AbstractBackgroundMultiple SARS-CoV-2 superspreading events suggest that aerosols play an important role in driving the COVID-19 pandemic. However, the detailed roles of coarse (>5μm) and fine (≤5μm) respiratory aerosols produced when breathing, talking, and singing are not well-understood.MethodsUsing a G-II exhaled breath collector, we measured viral RNA in coarse and fine respiratory aerosols emitted by COVID-19 patients during 30 minutes of breathing, 15 minutes of talking, and 15 minutes of singing.ResultsAmong the 22 study participants, 13 (59%) emitted detectable levels of SARS-CoV-2 RNA in respiratory aerosols, including 3 asymptomatic patients and 1 presymptomatic patient. Viral loads ranged from 63–5,821 N gene copies per expiratory activity per patient. Patients earlier in illness were more likely to emit detectable RNA, and loads differed significantly between breathing, talking, and singing. The largest proportion of SARS-CoV-2 RNA copies was emitted by singing (53%), followed by talking (41%) and breathing (6%). Overall, fine aerosols constituted 85% of the viral load detected in our study. Virus cultures were negative.ConclusionsFine aerosols produced by talking and singing contain more SARS-CoV-2 copies than coarse aerosols and may play a significant role in the transmission of SARS-CoV-2. Exposure to fine aerosols should be mitigated, especially in indoor environments where airborne transmission of SARS-CoV-2 is likely to occur. Isolating viable SARS-CoV-2 from respiratory aerosol samples remains challenging, and whether this can be more easily accomplished for emerging SARS-CoV-2 variants is an important enquiry for future studies.Key PointsWe sampled respiratory aerosols emitted by COVID-19 patients and discovered that fine aerosols (≤5μm) generated during talking and singing contain more SARS-CoV-2 copies than coarse aerosols (>5μm) and may play a significant role in the transmission of SARS-CoV-2.

PurposeCOVID-19, caused by Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2), has a wide disease spectrum ranging from asymptomatic to severe. While it is widely accepted that specific humoral immune responses are critical in controlling the infection, the relationship between the humoral immune response and disease severity is currently unclear.MethodsUsing a flow cytometry-based assay to detect specific antibodies against full length S protein, we compared the antibody levels between patients from different severity groups. We also analysed the cytokine profiles of patients from different severity groups by multiplex microbead-based immunoassay . ResultsWe found an association between specific IgM, IgA and IgG against the spike protein and disease severity. By comparing the ratio of Th1 IgG1 and IgG3 to Th2 IgG2 and IgG4, we observed that all severity groups exhibited a ratio that was skewed towards a stronger Th1 response over Th2 response. In addition to the strong Th1 response, patients with severe disease also developed a Th2 response, as exemplified by the smaller ratio of IgG1 and IgG3 over IgG2 and IgG4 and the smaller Th1/Th2 cytokine ratios, compared to patients with mild disease severity. ConclusionThe results suggest that acute severity or disease resolution is associated with a specific immunological phenotype. A smaller skew towards a Th1 response over Th2 response, during infection, may contribute to disease progression, while a greater skew towards a Th1 response over Th2 response may contribute to a better disease outcome. This may suggest potential therapeutic approaches to COVID-19 disease management.

Case identification is an ongoing issue for the COVID-19 epidemic, in particular for outpatient care where physicians must decide which patients to prioritise for further testing. This paper reports tools to classify patients based on symptom profiles based on 236 severe acute respiratory syndrome coronavirus 2 positive cases and 564 controls, accounting for the time course of illness using generalised multivariate logistic regression. Significant symptoms included abdominal pain, cough, diarrhoea, fever, headache, muscle ache, runny nose, sore throat, temperature between 37.5 and 37.9 °C and temperature above 38 °C, but their importance varied by day of illness at assessment. With a high percentile threshold for specificity at 0.95, the baseline model had reasonable sensitivity at 0.67. To further evaluate accuracy of model predictions, leave-one-out cross-validation confirmed high classification accuracy with an area under the receiver operating characteristic curve of 0.92. For the baseline model, sensitivity decreased to 0.56. External validation datasets reported similar result. Our study provides a tool to discern COVID-19 patients from controls using symptoms and day from illness onset with good predictive performance. It could be considered as a framework to complement laboratory testing in order to differentiate COVID-19 from other patients presenting with acute symptoms in outpatient care.

AbstractCase identification is an ongoing issue for the COVID-19 epidemic, in particular for outpatient care where physicians must decide which patients to prioritise for further testing. This paper reports tools to classify patients based on symptom profiles based on 236 SARS-CoV-2 positive cases and 564 controls, accounting for the time course of illness at point of assessment. Clinical differentiators of cases and controls were used to derive model-based risk scores. Significant symptoms included abdominal pain, cough, diarrhea, fever, headache, muscle ache, runny nose, sore throat, temperature between 37.5°C and 37.9°C, and temperature above 38°C, but their importance varied by day of illness at assessment. With a high percentile threshold for specificity at 0.95, the baseline model had reasonable sensitivity at 0.67. To further evaluate accuracy of model predictions, we firstly used leave-one-out cross-validation, which confirmed high classification accuracy with an area under the receiver operating characteristic curve of 0.92. For the baseline model, sensitivity decreased to 0.56. Secondly, in a separate ongoing prospective study of 237 COVID-19 and 346 primary care patients presenting with symptoms of acute respiratory infection, the baseline model had a sensitivity of 0.57 and specificity of 0.89, and in retrospective notes review of 100 COVID-19 cases diagnosed in primary care, sensitivity was 0.56. A web-app based tool has been developed for easy implementation as an adjunct to laboratory testing to differentiate COVID-19 positive cases among patients presenting in outpatient settings.

Given increasing antimicrobial resistance, we aimed to determine antibiotic susceptibility and presence of resistance genes in uropathogens in primary care, factors associated with resistance to commonly prescribed antibiotics, and effect of treatment on early symptom resolution. We conducted a prospective study of primary care patients with urinary tract infection (UTI) symptoms and culture-confirmed UTI in Singapore from 2015 to 2016.

ObjectiveTo better understand the barriers and facilitators to reporting and assessing what improvements would increase participation.IntroductionTraditional surveillance methods have a major challenge to estimating the burden of disease due to underreporting [1]. Participatory surveillance techniques can help supplement to monitor and detect foodborne outbreaks while reducing the impact of underreporting [2]. As there is a low participation rate in Singapore, this study aims to better understand the barriers and facilitators to reporting and assesses what improvements can increase participation.MethodsA total of 14 individuals participated in the study; 8 had informed health authorities of a possible foodborne outbreak while 6 patients were diagnosed with gastroenteritis at general practitioner (GP) clinics but did not report their illness to health authorities. We examined the barriers and facilitators to reporting foodborne illnesses to health authorities through semi-structured in-depth interviews and thematic analysis.ResultsThe median age of participants was 28 (Interquartile Range = 23-37). The majority were Singaporeans and had or were pursuing university qualifications. The combination of perceived severity of illness and degree of certainty of the cause of illness are key reasons that are both barrier and facilitator to reporting foodborne illness to the authorities. The informants expected government intervention and hoped that their actions would prevent others from being affected as well. However, reporting to health authorities was usually delayed by the participants’ severity of illness. Those who did not report were unaware of reporting channels and were concerned their actions would negatively affect food establishments.Participants also shared what they would like to see in a reporting system. Firstly, contact information should be easily accessible with a user-friendly system. Secondly, a human touch and live acknowledgement was desirable when reporting the issue instead of being met with recorded voice machine messages. This would also reduce the number of subsequent follow up calls from the authorities to gather information.ConclusionsIt is crucial for the public to be informed of easily accessible and user-friendly avenues to report foodborne incidences to the authorities. Being able to communicate directly to relevant authorities immediately would help relay issues with the assurance that the matter would be looked into and acted upon. In trying to understand the barriers and facilitators to reporting, the study hopes to see a higher motivation of the public to report, so that necessary actions can be taken to reduce foodborne incidences.References1. Fletcher SM, Lewis-Fuller E, Williams H, et al. Magnitude, distribution, and estimated level of underreporting of acute gastroenteritis in Jamaica. J Heal Popul Nutr. 2013;31(4 SUPPL.1):69-80. http://www.ncbi.nlm.nih.gov/pubmed/24992813. Accessed October 4, 2017.2. Quade P, Nsoesie EO. A Platform for Crowdsourced Foodborne Illness Surveillance: Description of Users and Reports. JMIR Public Heal Surveill. 2017;3(3):e42. doi:10.2196/publichealth.7076.

ObjectiveTo develop a forecasting model for weekly emergency department admissions due to pneumonia using information from hospital-based, community-based and laboratory-based surveillance systems.IntroductionPneumonia, an infection of the lung due to bacterial, viral or fungal pathogens, is a significant cause of morbidity and mortality worldwide. In the past few decades, the threat of emerging pathogens presenting as pneumonia, such as Severe Acute Respiratory Syndrome, avian influenza A(H5N1) and A(H7N9), and Middle East Respiratory Syndrome coronavirus has emphasised the importance of the surveillance of pneumonia and other severe respiratory infections. An unexpected increase in the number of hospital admissions for pneumonia or severe respiratory infections could be a signal of a change in the virulence of the influenza viruses or other respiratory pathogens circulating in the community, or an alert of an emerging pathogen which warrants further public health investigation.The purpose of this study was to develop a forecasting model to prospectively forecast the number of emergency department (ED) admissions due to pneumonia in Singapore, a tropical country. We hypothesise that there is complementary information between hospital-based and community-based surveillance systems. The clinical spectrum of many respiratory pathogens causing pneumonia ranges from asymptomatic or subclinical infection to severe or fatal pneumonia, and it is usually difficult to distinguish between the different pathogens in the absence of a laboratory test. Infected persons could present with varying degrees of severity of the infection, and seek treatment at different healthcare facilities. Hospital-based surveillance captures the more severe manifestation of the infection while community-based surveillance captures the less severe manifestation of the infection and enables earlier detection of the infection. Thus, the integration of information from the two surveillance systems should improve the prospective forecasting of ED admissions due to pneumonia. We also investigate if the inclusion of influenza data from the laboratory surveillance system would improve the forecasting model, since influenza circulates all-year round in Singapore and is a common aetiology for pneumonia.MethodsThis was a retrospective study using aggregated national surveillance data and meteorological data during the period 3 January 2011 to 1 January 2017.We compared the performance of autoregressive integrated moving average model (ARIMA) with multiple linear regression models with ARIMA errors, with and without the inclusion of influenza predictors at forecast horizons of 2, 4, 6 and 8 weeks in advance. Weekly data between the study period of 3 January 2011 and 1 January 2017 were split into training and validation sets, with the first three years of data used as the base training set. Time series cross validation was used to estimate the models’ accuracy and out-of-sample forecast accuracy was based on the calculation of the mean absolute error (MAE) and mean absolute percent forecast error (MAPE).ResultsThe multiple linear regression model with ARIMA errors that included influenza predictors was the best performing model while the basic ARIMA model was the worst performing model for all forecast horizons. The two multiple linear regression models with ARIMA errors had a MAPE of less than 10% for all forecast horizons.ConclusionsData from different multiple surveillance systems and the inclusion of influenza trends can be used to improve the forecast of ED admissions due to pneumonia in a tropical setting, despite the absence of large differences between seasons. Accurate forecasting at the national level can prepare healthcare facilities for an impending surge.

BACKGROUNDAntibody titres and vaccine effectiveness decline within 6 months after influenza vaccination in older adults. Biannual vaccination may be necessary to provide year-round protection in the tropics, where influenza circulates throughout the year.METHODSTropical Influenza Control Strategies (TROPICS1) was a single-center, 1:1 randomized, observer-blinded, active-comparator-controlled, superiority study in 200 community-resident adults aged ≥65 years. Participants received a standard-dose trivalent inactivated influenza vaccination (IIV3) at enrollment, and either tetanus-diphtheria-pertussis vaccination or IIV3 6 months later. The primary outcome was the proportion of participants with haemagglutination-inhibition (HI) geometric mean titre (GMT) ≥1:40 1 month after the second vaccination (month 7). Secondary outcomes included GMTs to month 12, the incidence of influenza-like illness (ILI), and adverse reactions after vaccination.RESULTSAt month 7, the proportion of participants with an HI tire ≥1:40 against A/H1N1 increased by 21.4% (95% confidence interval [CI] 8.6-33.4) in the semiannual vaccination group. This proportion was not significantly higher for A/H3N2 (4.3, 95% CI -1.1-10.8) or B (2.1, 95% CI -2.0-7.3). Semiannual vaccination significantly increased GMTs against A/H1N1 and A/H3N2, but not B, at month 7. Participants receiving a repeat vaccination of IIV3 reported a significantly lower incidence of ILI in the 6 months after the second vaccination (relative vaccine effectiveness 57.1%, 95% CI 0.6-81.5). The frequency of adverse events was similar after the first and second influenza vaccinations.CONCLUSIONSSemiannual influenza vaccination in older residents of tropical countries has the potential to improve serological measures of protection against infection. Alternative vaccination strategies should also be studied.CLINICAL TRIALS REGISTRATIONNCT02655874.

The mammary gland undergoes fast cell proliferation during early pregnancy, yet the mechanism to ensure genome integrity during this highly proliferative stage is largely unknown. We show that pregnancy triggers replicative stresses leading to genetic instability in mice carrying a mammary specific disruption of breast cancer associated gene-1 (BRCA1). The fast cell proliferation was correlated with enhanced expression of most genes encoding replisomes, which are positively regulated by estrogen/ERα signaling but negatively regulated by BRCA1. Our further analysis revealed two parallel signaling pathways, which are mediated by ATR-CHK1 and WEE1-MCM2 and are responsible for regulating DNA replication checkpoint. Upon DNA damage, BRCA1 deficiency markedly enhances DNA replication initiation and preferably impairs DNA replication checkpoint mediated by ATR and CHK1. Meanwhile, DNA damage also activates WEE1-MCM2 signaling, which inhibits DNA replication initiation and enables BRCA1-deficient cells to avoid further genomic instability. Finally, we demonstrated that overriding this defense by WEE1 inhibition in combination with cisplatin, which causes DNA damage, serves as a promising therapeutic approach for killing BRCA1-deficient cancer cells.

Background Although peer assessment has been used for evaluating performance of medical students and practicing doctors, it has not been studied as a method to distribute a common group work mark equitably to medical students working in large groups where tutors cannot observe all students constantly. Methods The authors developed and evaluated a mathematical formulation whereby a common group mark could be distributed among group members using peer assessment of individual contributions to group work, maintaining inter-group variation in group work scores. This was motivated by community health projects undertaken by large groups of year four medical students at the National University of Singapore, and the new and old formulations are presented via application to 263 students in seven groups of 36 to 40 during the academic year 2012/2013. Results This novel formulation produced a less clustered mark distribution that rewarded students who contributed more to their team. Although collusion among some members to form a voting alliance and ‘personal vendettas’ were potential problems, the former was not detected and the latter had little impact on the overall grade a student received when working in a large group. The majority of students thought the new formulation was fairer. Conclusions The new formulation is easy to implement and arguably awards grades more equitably in modules where group work is a major component.