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The student population has been highly vulnerable to the risk of mental health deterioration during the coronavirus disease (COVID-19) pandemic. This study aimed to reveal the prevalence and predictors of mental health among students in Poland, Slovenia, Czechia, Ukraine, Russia, Germany, Turkey, Israel, and Colombia in a socioeconomic context during the COVID-19 pandemic. The study was conducted among 2349 students (69% women) from May–July 2020. Data were collected by means of the Generalized Anxiety Disorder (GAD-7), Patient Health Questionnaire (PHQ-8), Perceived Stress Scale (PSS-10), Gender Inequality Index (GII), Standard & Poor's Global Ratings, the Oxford COVID-19 Government Response Tracker (OxCGRT), and a sociodemographic survey. Descriptive statistics and Bayesian multilevel skew-normal regression analyses were conducted. The prevalence of high stress, depression, and generalized anxiety symptoms in the total sample was 61.30%, 40.3%, and 30%, respectively. The multilevel Bayesian model showed that female sex was a credible predictor of PSS-10, GAD-7, and PHQ-8 scores. In addition, place of residence (town) and educational level (first-cycle studies) were risk factors for the PHQ-8. This study showed that mental health issues are alarming in the student population. Regular psychological support should be provided to students by universities.

Key Points Precision medicine seeks to identify and classify individual patients so that optimal treatment decisions can be made. Nations are recognizing this area of research by developing national cohorts from which to collect data and developing regulatory guidelines on biomarkers. The cost of genetic sequencing and other 'omics' technologies has decreased while the quality of data they generate has increased. Thus, immense amounts of molecular data are being derived from cohort studies to begin developing new biomarkers to classify patients into subtypes. The development of biomarkers is largely limited by the following factors: low statistical power in rare subtypes; risk of false-positive findings in studies that do not validate their findings in a separate cohort and/or conduct concomitant mechanistic experiments; and technical reproducibility concerns. Genomics and protein immunohistochemistry have led the way for developing biomarkers. Other molecular measurements (for example, metabolomics and microbiomics) are still in preliminary stages and are often not validated in another cohort. Integrating different types of molecules into a biomarker panel, along with other patient data, is the future of precision medicine; however, the sheer number of potential combinations of data types complicates the concerns about statistical power and reproducibility. Currently, improved biomarkers are needed to differentiate lung nodules identified by new US national screening recommendations into non-cancer, cancer with poor survival probability and cancer with higher survival probability subtypes, to provide thousands of individuals with precise treatment decisions. This Review summarizes the successes and challenges of using different types of molecules as biomarkers, using lung cancer as a key illustrative example. This article also discusses the future of precision medicine and national-level efforts to better treat patients with cancer. Precision medicine relies on validated biomarkers with which to better classify patients by their probable disease risk, prognosis and/or response to treatment. Although affordable 'omics'-based technology has enabled faster identification of putative biomarkers, the validation of biomarkers is still stymied by low statistical power and poor reproducibility of results. This Review summarizes the successes and challenges of using different types of molecule as biomarkers, using lung cancer as a key illustrative example. Efforts at the national level of several countries to tie molecular measurement of samples to patient data via electronic medical records are the future of precision medicine research.

Despite the accumulating evidence that increased consumption of ultra-processed food has adverse health implications, it remains difficult to decide what constitutes processed food. Indeed, the current processing-based classification of food has limited coverage and does not differentiate between degrees of processing, hindering consumer choices and slowing research on the health implications of processed food. Here we introduce a machine learning algorithm that accurately predicts the degree of processing for any food, indicating that over 73% of the US food supply is ultra-processed. We show that the increased reliance of an individual’s diet on ultra-processed food correlates with higher risk of metabolic syndrome, diabetes, angina, elevated blood pressure and biological age, and reduces the bio-availability of vitamins. Finally, we find that replacing foods with less processed alternatives can significantly reduce the health implications of ultra-processed food, suggesting that access to information on the degree of processing, currently unavailable to consumers, could improve population health. Evidence suggests that increased consumption of ultra-processed food has adverse health implications, however, it remains difficult to classify processed food. Here, the authors introduce FPro , a machine learning-based score predicting the degree of food processing.

Parkinson’s disease is a progressive neurodegenerative movement disorder with a long latent phase and currently no disease-modifying treatments. Reliable predictive biomarkers that could transform efforts to develop neuroprotective treatments remain to be identified. Using UK Biobank, we investigated the predictive value of accelerometry in identifying prodromal Parkinson’s disease in the general population and compared this digital biomarker with models based on genetics, lifestyle, blood biochemistry or prodromal symptoms data. Machine learning models trained using accelerometry data achieved better test performance in distinguishing both clinically diagnosed Parkinson’s disease ( n  = 153) (area under precision recall curve (AUPRC) 0.14 ± 0.04) and prodromal Parkinson’s disease ( n  = 113) up to 7 years pre-diagnosis (AUPRC 0.07 ± 0.03) from the general population ( n  = 33,009) compared with all other modalities tested (genetics: AUPRC = 0.01 ± 0.00, P  = 2.2 × 10 −3 ; lifestyle: AUPRC = 0.03 ± 0.04, P  = 2.5 × 10 −3 ; blood biochemistry: AUPRC = 0.01 ± 0.00, P  = 4.1 × 10 −3 ; prodromal signs: AUPRC = 0.01 ± 0.00, P  = 3.6 × 10 −3 ). Accelerometry is a potentially important, low-cost screening tool for determining people at risk of developing Parkinson’s disease and identifying participants for clinical trials of neuroprotective treatments. UK Biobank moement tracking data show increased performance as compared to symptoms and genetic and lifestyle factors in identifying prodromal Parkinson’s disease in the general population.

For COVID-19, it is vital to understand if quarantines shorter than 14 days can be equally effective with judiciously deployed testing. Here, we develop a mathematical model that quantifies the probability of post-quarantine transmission incorporating testing into travel quarantine, quarantine of traced contacts with an unknown time of infection, and quarantine of cases with a known time of exposure. We find that testing on exit (or entry and exit) can reduce the duration of a 14-day quarantine by 50%, while testing on entry shortens quarantine by at most one day. In a real-world test of our theory applied to offshore oil rig employees, 47 positives were obtained with testing on entry and exit to quarantine, of which 16 had tested negative at entry; preventing an expected nine offshore transmission events that each could have led to outbreaks. We show that appropriately timed testing can make shorter quarantines effective. Safely reducing the necessary duration of quarantine for COVID-19 could lessen the economic impacts of the pandemic. Here, the authors demonstrate that testing on exit from quarantine is more effective than testing on entry, and can enable quarantine to be reduced from fourteen to seven days.

Large-scale collection of data could help curb the COVID-19 pandemic, but it should not neglect privacy and public trust. Best practices should be identified to maintain responsible data-collection and data-processing standards at a global scale.

BackgroundHuman papillomavirus (HPV) testing on self-samples represents a great opportunity to increase cervical cancer screening uptake among under-screened women.MethodsA systematic review and meta-analysis on randomised controlled trials (RCTs) were performed to update the evidence on the efficacy of strategies for offering self-sampling kits for HPV testing compared to conventional invitations and to compare different self-sampling invitation scenarios. Four experimental invitational scenarios were considered. Women in the control group were invited for screening according to existing practice: collection of a cervical specimen by a healthcare professional. Random-effects models were used to pool proportions, relative participation rates and absolute participation differences.ResultsThirty-three trials were included. In the intention-to-treat analysis, all self-sampling invitation scenarios were more effective in reaching under-screened women compared to controls. Pooled participation difference (PD) and 95% confidence interval (CI) for experimental vs. control was 13.2% (95% CI = 11.0–15.3%) for mail-to-all, 4.4% (95% CI = 1.2–7.6%) for opt-in, 39.1% (95% CI = 8.4–69.9%) for community mobilisation & outreach and 28.1% (23.5–32.7%) for offer at healthcare service. PD for the comparison opt-in vs. mail-to-all, assessed in nine trials, was −8.2% (95% CI = −10.8 to −5.7%).DiscussionOverall, screening participation was higher among women invited for self-sampling compared to control, regardless of the invitation strategy used. Opt-in strategies were less effective than send-to-all strategies.

Artificial intelligence (AI) has the potential to improve breast cancer screening; however, prospective evidence of the safe implementation of AI into real clinical practice is limited. A commercially available AI system was implemented as an additional reader to standard double reading to flag cases for further arbitration review among screened women. Performance was assessed prospectively in three phases: a single-center pilot rollout, a wider multicenter pilot rollout and a full live rollout. The results showed that, compared to double reading, implementing the AI-assisted additional-reader process could achieve 0.7–1.6 additional cancer detection per 1,000 cases, with 0.16–0.30% additional recalls, 0–0.23% unnecessary recalls and a 0.1–1.9% increase in positive predictive value (PPV) after 7–11% additional human reads of AI-flagged cases (equating to 4–6% additional overall reading workload). The majority of cancerous cases detected by the AI-assisted additional-reader process were invasive (83.3%) and small-sized (≤10 mm, 47.0%). This evaluation suggests that using AI as an additional reader can improve the early detection of breast cancer with relevant prognostic features, with minimal to no unnecessary recalls. Although the AI-assisted additional-reader workflow requires additional reads, the higher PPV suggests that it can increase screening effectiveness. In a phased prospective rollout, the implementation of AI as an additional reader for mammography screening improved the real-world early detection of breast cancer compared to standard double reading involving two independent radiologists.

Effective screening of SARS-CoV-2 enables quick and efficient diagnosis of COVID-19 and can mitigate the burden on healthcare systems. Prediction models that combine several features to estimate the risk of infection have been developed. These aim to assist medical staff worldwide in triaging patients, especially in the context of limited healthcare resources. We established a machine-learning approach that trained on records from 51,831 tested individuals (of whom 4769 were confirmed to have COVID-19). The test set contained data from the subsequent week (47,401 tested individuals of whom 3624 were confirmed to have COVID-19). Our model predicted COVID-19 test results with high accuracy using only eight binary features: sex, age ≥60 years, known contact with an infected individual, and the appearance of five initial clinical symptoms. Overall, based on the nationwide data publicly reported by the Israeli Ministry of Health, we developed a model that detects COVID-19 cases by simple features accessed by asking basic questions. Our framework can be used, among other considerations, to prioritize testing for COVID-19 when testing resources are limited.

Since the Opening of China, the country’s economy has continuously and rapidly improved. Various economic, educational, and health policies have been implemented to shape the development of society, which may have greatly affected the Chinese diet and related malnutrition issues. The objective of the present review was to comprehensively review long-term trends in dietary intakes, nutrition status, and subsequent health challenges among Chinese adults. The data sources were mainly the 1982, 1992, 2002, and 2010–2012 China National Nutrition Surveys (CNNS) and reports and the 1989–2015 China Health and Nutrition Survey (CHNS). Over decades, there have been significant changes in the dietary structure of Chinese adults, characterized as decreased intake of cereals and vegetables and increased intake of animal foods with pork dominating. Intakes of eggs, fish, and dairy has reminded at a low level, with only a small increase over time. Consumption of cooking oil and salt was substantively far above the recommendations. A great proportion of fat-to-energy intake and “hidden hunger” was still prominent. Despite nutrition deficiency, there have been some modest improvements in related diseases, but overweight and obesity has become a prominent issue, with the prevalence in adults increasing from 16.4% and 3.6% in 1982 to 30.1% and 11.9% in 2012, respectively. In conclusion, this review sheds light on some salient problems with nutrition and malnutrition status in China, especially the dual challenges of undernutrition and overnutrition. Dynamic monitoring of nutritional characteristics in China should be strengthened, and effective strategies to improve nutrition need to be targeted at the national, societal, family, and individual levels.