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Undiagnosed cognitive impairment is a pervasive global issue, often due to subtle nature of early symptoms, necessitating the use of brief cognitive tests for early detection. However, most brief tests are not scalable (requiring trained professionals), and are not designed for lower literacy groups (e.g. in underserved communities). Here, we developed PENSIEVE-AI TM , a drawing-based digital test that is less dependent on literacy, and can be self-administered in <5 min. In a prospective study involving 1758 community-dwelling individuals aged 65 and older from Singapore (education range = 0–23 years), our deep-learning model showed excellent performance in detecting clinically-adjudicated mild cognitive impairment and dementia (AUC = 93%), comparable to traditional neuropsychological assessments (AUC = 94%, P comparison  = 1.000). Results were consistent even across education subgroups. Being less dependent on literacy, PENSIEVE-AI holds promise for broader deployment in literacy-diverse populations similar to Singapore (e.g. some Asian and lower- and middle-income countries), potentially improving early detection and intervention of cognitive impairment. PENSIEVE-AI is a drawing-based, digital cognitive test that can be self-administered in <5 min. It matches traditional tests in detecting cognitive impairment and dementia, offering promise for early detection in literacy-diverse populations.

Prospective memory (PM), the ability to recall future intentions, is crucial for independent living. Impairment of PM is a common complaint following head injury and is a significant impediment to good recovery, yet no studies have explored PM in mild traumatic brain injury (mTBI). In this study, prospective memory was examined in 31 mTBI patients and matched controls within a month of injury and 3 months after. mTBI patients performed more poorly than controls on the MIST task (Raskin, 2004) within the first month following injury, indicating that PM impairment is part of the acute cognitive sequelae of mTBI. These problems persisted beyond 3 months post-injury, suggesting that PM may be a sensitive indicator of cerebral compromise in mild brain injuries.

Abstract Objectives To describe antimicrobial use (AMU) in patients admitted to hospitals in Timor-Leste. Methods In 2020 and 2021, we undertook antimicrobial prescribing point prevalence surveys across all six hospitals in Timor-Leste (one national and five municipal) to describe AMU and appropriateness in admitted patients. Results In 2020, 291/394 (73.9%) surveyed patients had been prescribed antimicrobials, compared with 260/403 (64.5%) in 2021 (P = 0.004). Most (309/551; 56.1%) were prescribed one antimicrobial, and 179/551 (32.5%) were prescribed two. The most commonly prescribed antibiotics were ceftriaxone (38.5% in 2020, 41.5% in 2021) and ampicillin (35.7% in 2020, 32.3% in 2021), followed by gentamicin, metronidazole and cloxacillin. Reserve antibiotics like meropenem and vancomycin were minimally used. Of all antimicrobial prescriptions, 70.8% were deemed appropriate in 2020 and 69.1% in 2021. Antimicrobial prescriptions for surgical and post-partum prophylaxis were frequently deemed inappropriate [37/50 (74.0%) and 39/44 (88.6%) prescriptions, respectively]. Conclusions Most patients admitted to hospital in Timor-Leste are prescribed antimicrobials, and approximately one-third of these prescriptions are inappropriate. However, this was in the context of limited local guideline availability at the time of surveys and limited microbiological culture capacity outside of the capital, Dili. Improved microbiological guidance, iterative guideline revisions based on local antimicrobial resistance (AMR) surveillance data, and enhanced stewardship activities including further point prevalence studies, could improve antimicrobial use, optimize patient outcomes and reduce AMR in Timor-Leste.

We aim to describe a case series of critically and non-critically ill COVID-19 patients in Singapore. This was a multicentered prospective study with clinical and laboratory details. Details for fifty uncomplicated COVID-19 patients and ten who required mechanical ventilation were collected. We compared clinical features between the groups, assessed predictors of intubation, and described ventilatory management in ICU patients. Ventilated patients were significantly older, reported more dyspnea, had elevated C-reactive protein and lactate dehydrogenase. A multivariable logistic regression model identified respiratory rate (aOR 2.83, 95% CI 1.24–6.47) and neutrophil count (aOR 2.39, 95% CI 1.34–4.26) on admission as independent predictors of intubation with area under receiver operating characteristic curve of 0.928 (95% CI 0.828–0.979). Median APACHE II score was 19 (IQR 17–22) and PaO2/FiO2 ratio before intubation was 104 (IQR 89–129). Median peak FiO2 was 0.75 (IQR 0.6–1.0), positive end-expiratory pressure 12 (IQR 10–14) and plateau pressure 22 (IQR 18–26) in the first 24 h of ventilation. Median duration of ventilation was 6.5 days (IQR 5.5–13). There were no fatalities. Most COVID-19 patients in Singapore who required mechanical ventilation because of ARDS were extubated with no mortality.

Gene expression signatures (“molecular phenotypes”) are extensively utilized in cancer research. Using direct capture Perturb-seq (dcPerturb-seq), we surveyed how molecular phenotypes in gastric cancer (GC) are shaped by cell-intrinsic genetic alterations interacting with cell-extrinsic therapeutic exposures. Interrogating >200 GC-related genes against 4 distinct epigenetic drug classes across multiple gastric lines, we captured 18.9 million pharmacogenomic expression interactions in 669,065 cells representing baseline and post-therapeutic molecular phenotypes. This single-cell pharmacogenomic compendium confirmed previously known gene-driven molecular phenotypes (TP53, PCNA), elucidated poorly characterized genes (ZBTB41), and uncovered novel gene dosage-molecular phenotype relationships. Molecular phenotypes in post-therapeutic surviving cells revealed distinct gene perturbation-induced pathways causing convergent drug resistance (EMT plasticity, cell cycle alterations, metabolic reprogramming), with MED12, EP300 and KLF4 modulating histone deacetylase inhibitor (HDACi) resistance. Mapping of in vitro molecular phenotypes to primary human GCs imparted prognostic information and insights into spatial heterogeneity and targeted therapy response. Our results illustrate how dcPerturb-seq can systematically and comprehensively map diverse cancer-associated molecular phenotypes across multiple gene-gene and gene-drug interactions, yielding translational insights. By integrating single-cell perturbation screens with epigenetic drug profiling in gastric cancer, we systematically resolve how cell-intrinsic genomic pathways interact with extrinsic drug-induced responses at unprecedented resolution. Analyzing 18.9 million pharmacogenomic expression interactions across >200 cancer-related genes in 669,065 cells, our approach revealed previously unknown functional annotations, dosage-dependent effects, and convergent patterns of therapeutic resistance with direct clinical relevance. Our results provide a high-resolution roadmap contributing to a comprehensive encyclopedia of all cancer-related molecular phenotypes, accelerating translational cancer research.