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Digital health research plays a vital role in advancing equitable health care. The diversity of research teams is thereby instrumental in capturing societal challenges, increasing productivity, and reducing bias in algorithms. Despite its importance, the gender distribution within digital health authorship remains largely unexplored. This study aimed to investigate the gender distribution among first and last authors in digital health research, thereby identifying predicting factors of female authorship. This bibliometric analysis examined the gender distribution across 59,980 publications from 1999 to 2023, spanning 42 digital health journals indexed in the Web of Science. To identify strategies ensuring equality in research, a detailed comparison of gender representation in JMIR journals was conducted within the field, as well as against a matched sample. Two-tailed Welch 2-sample t tests, Wilcoxon rank sum tests, and chi-square tests were used to assess differences. In addition, odds ratios were calculated to identify predictors of female authorship. The analysis revealed that 37% of first authors and 30% of last authors in digital health were female. JMIR journals demonstrated a higher representation, with 49% of first authors and 38% of last authors being female, yielding odds ratios of 1.96 (95% CI 1.90-2.03; P<.001) and 1.78 (95% CI 1.71-1.84; P<.001), respectively. Since 2008, JMIR journals have consistently featured a greater proportion of female first authors than male counterparts. Other factors that predicted female authorship included having female authors in other relevant positions and gender discordance, given the higher rate of male last authors in the field. There was an evident shift toward gender parity across publications in digital health, particularly from the publisher JMIR Publications. The specialized focus of its sister journals, equitable editorial policies, and transparency in the review process might contribute to these achievements. Further research is imperative to establish causality, enabling the replication of these successful strategies across other scientific fields to bridge the gender gap in digital health effectively.

The potential of artificial intelligence (AI)-based large language models, such as ChatGPT, has gained significant attention in the medical field. This enthusiasm is driven not only by recent breakthroughs and improved accessibility, but also by the prospect of democratizing medical knowledge and promoting equitable health care. However, the performance of ChatGPT is substantially influenced by the input language, and given the growing public trust in this AI tool compared to that in traditional sources of information, investigating its medical accuracy across different languages is of particular importance. This study aimed to compare the performance of GPT-3.5 and GPT-4 with that of medical students on the written German medical licensing examination. To assess GPT-3.5's and GPT-4's medical proficiency, we used 937 original multiple-choice questions from 3 written German medical licensing examinations in October 2021, April 2022, and October 2022. GPT-4 achieved an average score of 85% and ranked in the 92.8th, 99.5th, and 92.6th percentiles among medical students who took the same examinations in October 2021, April 2022, and October 2022, respectively. This represents a substantial improvement of 27% compared to GPT-3.5, which only passed 1 out of the 3 examinations. While GPT-3.5 performed well in psychiatry questions, GPT-4 exhibited strengths in internal medicine and surgery but showed weakness in academic research. The study results highlight ChatGPT's remarkable improvement from moderate (GPT-3.5) to high competency (GPT-4) in answering medical licensing examination questions in German. While GPT-4's predecessor (GPT-3.5) was imprecise and inconsistent, it demonstrates considerable potential to improve medical education and patient care, provided that medically trained users critically evaluate its results. As the replacement of search engines by AI tools seems possible in the future, further studies with nonprofessional questions are needed to assess the safety and accuracy of ChatGPT for the general population.

Cell morphology determines cell behavior, signal transduction, protein-protein interaction, and responsiveness to external stimuli. In cancer, these functions profoundly contribute to resistance mechanisms to radio- and chemotherapy. With regard to this aspect, this study compared the genome wide gene expression in exponentially growing cell lines from different tumor entities, lung carcinoma and squamous cell carcinoma, under more physiological three-dimensional (3D) versus monolayer cell culture conditions. Whole genome cDNA microarray analysis was accomplished using the Affymetrix HG U133 Plus 2.0 gene chip. Significance analysis of microarray (SAM) and t-test analysis revealed significant changes in gene expression profiles of 3D relative to 2D cell culture conditions. These changes affected the extracellular matrix and were mainly associated with biological processes like tissue development, cell adhesion, immune system and defense response in contrast to terms related to DNA repair, which lacked significant alterations. Selected genes were verified by semi-quantitative RT-PCR and Western blotting. Additionally, we show that 3D growth mediates a significant increase in tumor cell radio- and chemoresistance relative to 2D. Our findings show significant gene expression differences between 3D and 2D cell culture systems and indicate that cellular responsiveness to external stress such as ionizing radiation and chemotherapeutics is essentially influenced by differential expression of genes involved in the regulation of integrin signaling, cell shape and cell-cell contact.

In an increasingly interconnected health care system, laboratory medicine can facilitate diagnosis and treatment of patients effectively. This article describes necessary changes and points to potential challenges on a technical, content, and organizational level. As a technical precondition, electronic laboratory reports have to become machine-readable and interpretable. Terminologies such as Logical Observation Identifiers Names and Codes (LOINC), Nomenclature for Properties and Units (NPU), Unified Code for Units of Measure (UCUM), and SNOMED-CT can lead to the necessary semantic interoperability. Even if only single “atomized” results of the whole report are extracted, the necessary information for correct interpretation must be available. Therefore, interpretive comments, e.g., concerns about an increased measurement uncertainty must be electronically attached to every affected measurement result. Standardization of laboratory analyses with traceable standards and reference materials will enable knowledge transfer and safe interpretation of laboratory analyses from multiple laboratories. In an interconnected health care system, laboratories should strive to transform themselves into a data hub that not only receives samples but also extensive information about the patient. On that basis, they can return measurement results enriched with high-quality interpretive comments tailored to the individual patient and unlock the full potential of laboratory medicine.

This observational study investigated the impact of hyponatremia resolution on the results of a comprehensive geriatric assessment (CGA) in 150 patients with age ≥70 years and serum sodium <130 mEq/L. The test battery including Barthel index of Activities of Daily Living (ADL) and various tests of neurocognitive function, motor performance and mood stability was applied on admission and at discharge. Changes of individual test results (Δ) were analyzed and normonatremic patients matched for age, gender, and ADL served as reference group. Most CGA test results improved. The improvement was more pronounced in the hyponatremia group with respect to ADL (ΔADL: 14.3 ± 17.1 vs. 9.8 ± 14.7; p = 0.002) and MMSE (ΔMMSE: 1.8 ± 3.0 vs. 0.7 ± 1.9; p = 0.002). Effect sizes were small (i.e., >0.2) in the overall analysis for ΔADL and ΔMMSE and moderate (i.e., >0.5) for ΔMMSE in the euvolemic subgroup. Beneficial effects on ΔADL and ΔMMSE were only observed in the subgroup of patients in which [Na + ] was raised by >5 mEq/L and multivariable linear regression analysis confirmed [Na + ] increase to be an independent predictor of MMSE improvement. Resolution of hyponatremia has a beneficial impact on the geriatric patients’ overall functional status, in particular in euvolemic cases.

Aim of this study was to assess the biological function in tumor progression and metastatic process carcinoembryonic antigen-related cell adhesion molecules (CEACAM) 1, 5 and 6 in pancreatic adenocarcinoma (PDAC). CEACAM knock down cells were established and assessed in vitro and in a subcutaneous and intraperitoneal mouse xenograft model. Tissue and serum expression of patients with PDAC were assessed by immunohistochemistry (IHC) and by enzyme linked immunosorbent assays. Presence of lymph node metastasis was correlated with CEACAM 5 and 6 expression (determined by IHC) and tumor recurrence exclusively with CEACAM 6. Patients with CEACAM 5 and 6 expression showed a significantly shortened OS in Kaplan-Meier survival analyses. Elevated CEACAM6 serum values showed a correlation with distant metastasis and. Survival analysis revealed a prolonged OS for patients with low serum CEACAM 1 values. In vitro proliferation and migration capacity was increased in CEACAM knock down PDAC cells, however, mice inoculated with CEACAM knock down cells showed a prolonged overall-survival (OS). The number of spontaneous pulmonary metastasis was increased in the CEACAM knock down group. The effects mediated by CEACAM expression in PDAC are complex, though overexpression is correlated with loco-regional aggressive tumor growth. However, loss of CEACAM can be considered as a part of epithelial-mesenchymal transition and is therefore of rather importance in the process of distant metastasis.

Left ventricular dysfunction is a frequent and potentially severe side effect of many tyrosine kinase inhibitors (TKI). The mode of toxicity is not identified, but may include impairment of mitochondrial or sarcomeric function, autophagy or angiogenesis, either as an on-target or off-target mechanism. We studied concentration-response curves and time courses for nine TKIs in three-dimensional, force generating engineered heart tissue (EHT) from neonatal rat heart cells. We detected a concentration- and time-dependent decline in contractile force for gefitinib, lapatinib, sunitinib, imatinib, sorafenib, vandetanib and lestaurtinib and no decline in contractile force for erlotinib and dasatinib after 96 hours of incubation. The decline in contractile force was associated with an impairment of autophagy (LC3 Western blot) and appearance of autophagolysosomes (transmission electron microscopy). This study demonstrates the feasibility to study TKI-mediated force effects in EHTs and identifies an association between a decline in contractility and inhibition of autophagic flux.

Immunoassays are a standard diagnostic tool that assesses immunity in severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) infection. However, immunoassays do not provide information about contaminating antigens or cross-reactions and might exhibit inaccurately high sensitivity and low specificity. We aimed to gain insight into the serological immune response of SARS-CoV-2 patients by immunoblot analysis. We analyzed serum immunoglobulins IgM, -A, and -G directed against SARS-CoV-2 proteins by immunoblot analysis from 12 infected patients. We determined IgG isotype antibodies by commercially available ELISA and assessed the clinical parameters of inflammation status and kidney and liver injury. Unexpectedly, we found no correlation between the presence of antibodies and the future course of the disease. However, attention should be paid to the parameters CRP, IL-6, and LDH. We found evidence of antibody cross-reactivity, which questions the reliability of results for serum samples that tested negative for anti-SARS-CoV-2 antibodies when assessed by immunoassays. Nevertheless, for the detection of IgG anti-SARS-CoV-2 antibodies, our data suggest that the use of the spike glycoprotein in immunoassays should be sufficient to identify positive patients. Using a combination of the spike glycoprotein and the open reading frame 8 protein could prove to be the best way of detecting anti-SARS-CoV-2 IgM antibodies.

Beneficial effects of vitamin D on COVID-19 progression have been discussed in several studies. Vitamin D stimulates the expression of the antimicrobial peptide LL-37, and evidence shows that LL-37 can antagonize SARS-CoV-2. Therefore, we investigated the association between LL-37 and vitamin D serum levels and the severity of COVID-19. To this end, 78 COVID-19 patients were divided into 5 groups according to disease severity. We determined serum levels of LL-37, vitamin D, and routine laboratory parameters. We demonstrated a correlation of CRP, IL-6, PCT, leukocyte count, and LDH with the severity of COVID-19. Our study did not demonstrate a direct relationship between serum levels of LL-37 and vitamin D and the severity of COVID-19. LL-37 is produced by granulocytes and released at the site of inflammation. Therefore, the analysis of LL-37 in broncho-alvelolar lavage rather than in patient serum seems critical. However, since LL-37 is produced by granulocytes, we determined serum LL-37 levels as a function of leukocyte count. The LL-37/leukocyte count ratio correlates highly significantly inversely proportional with COVID-19 severity. Our results indicate that the LL-37/leukocyte count ratio could be used to assess the risk of COVID-19 progression as early as hospital admission.

Despite the transformative potential of artificial intelligence (AI)-based chatbots in medicine, their implementation is hindered by data privacy and security concerns. DeepSeek offers a conceivable solution through its capability for local offline operations. However, as of 2025, it remains unclear whether DeepSeek can achieve an accuracy comparable to that of conventional, cloud-based AI chatbots. This study aims to evaluate whether DeepSeek, an AI-based chatbot capable of offline operation, achieves answer accuracy on medical multiple-choice questions (MCQs) comparable to that of leading chatbots (ie, ChatGPT and Gemini) on German medical MCQs, thereby assessing its potential as a privacy-preserving alternative for clinical use. A total of 200 interdisciplinary MCQs from the German Progress Test Medicine were administered to ChatGPT (GPT-o3-mini), DeepSeek (DeepSeek-R1), and Gemini (Gemini 2.0 Flash). Accuracy was defined as the proportion of correctly solved questions. Overall differences among the 3 models were tested with the Cochran Q test, while pairwise comparisons were conducted using the McNemar test. Subgroup analyses were performed by medical domain (Fisher exact test) and question length (Wilcoxon rank-sum test). An a priori power analysis indicated a minimum sample size of 195 questions. All 3 chatbots surpassed the conventional passing threshold of 60%, with accuracies of 96% (192/200) for DeepSeek, 94% (188/200) for Gemini, and 92.5% (185/200) for ChatGPT. The overall difference among models was not statistically significant (P=.10) nor were pairwise comparisons. However, incorrect responses were significantly associated with longer question length for DeepSeek (P=.049) and ChatGPT (P=.04) but not for Gemini. No significant differences in performance were observed across clinical versus preclinical domains or medical specialties (all P>.05). Overall, DeepSeek demonstrates outstanding performance on German medical MCQs comparable to the widely used chatbots ChatGPT and Gemini. Similar to ChatGPT, DeepSeek's performance declined with increasing question length, highlighting verbosity as a persistent challenge for large language models. While DeepSeek's offline capability and lower operational costs are advantageous, its safe and reliable application in clinical contexts requires further investigation.