Purpose: Immune checkpoint inhibitor plus chemotherapy is the standard first-line treatment for advanced gastric cancer; however, predictive biomarkers for optimal patient selection remain unsatisfactory. This study was aimed at evaluating the predictive value of tumor mutational burden (TMB) and insertion/deletion (Indel) rate in patients with gastric cancer treated with nivolumab plus chemotherapy. Materials and Methods: This retrospective study included 132 patients with gastric cancer treated with first-line nivolumab plus chemotherapy and 185 patients treated with chemotherapy alone, all of whom had next-generation sequencing data available. The TMB and Indel cut-offs were set at 15.63 mutations per megabase and 18.19%, respectively, as determined based on their ability to best distinguish progression-free survival (PFS) among the patients who received nivolumab plus chemotherapy. Results: PFS was favorable for nivolumab and chemotherapy than for chemotherapy alone in both the high and low TMB groups; nevertheless, survival benefits were observed only in the high Indel group. Among the subgroups defined based on both TMB and Indel rates, the high TMB and high Indel rate subgroup showed the greatest benefit from nivolumab plus chemotherapy compared with that from chemotherapy alone. The benefit of this subgroup remained significant in patients with proficient mismatch repair (MMR) tumors, whose survival outcomes were comparable to those of patients with deficient MMR tumors.Among patients treated with nivolumab plus chemotherapy, high TMB and Indel rate were independently associated with favorable survival outcomes. Conclusions Thus, Indel rate, particularly in combination with TMB, may be a promising predictive biomarker for gastric cancer. However, further validation of their predictive value is warranted.

The gastrointestinal stromal tumor (GIST) is one of the most common mesenchymal tumors of the gastrointestinal tract. Between the 1990s and early 2000s, GIST was identified as a tumor characterized by KIT or PDGFRA mutations, resulting in imatinib being established as an effective targeted therapy. However, with advances in molecular diagnostics, approximately 10%–15% of GISTs have been reported to harbor alternative mutations, such as those in the succinate dehydrogenase subunit genes and BRAF, leading to the development of additional targeted therapies. GISTs exhibit a wide spectrum of clinical behaviors, ranging from indolent to highly aggressive, prompting the development of diverse risk classification systems. However, multiple systems remain in use, leading to inconsistent pathologic reports. Moreover, the mitotic counting method—a key factor in risk stratification—has become a major source of confusion among pathologists owing to the adoption of digital pathology and discrepancies between updated international guidelines and outdated reimbursement requirements. These inconsistencies have hindered pathologic reporting and communication between pathologists and clinicians. This review comprehensively overviews the historical background, molecular subtypes, and risk classification systems of GIST, focusing on evolving issues in mitotic rate evaluation and the application of risk classification systems in clinical practice.

Although venous invasion (VI) is associated with distant metastasis and observed in >50% of pT2–4 colorectal cancers (CRCs), the role of VI in pT1 CRCs is not well-defined. Methods: Thirty-four surgically resected pT1 CRCs were reevaluated for 2-dimensional (2D) VI using hematoxylin and eosin (H&E)–stained slides with additional elastic and desmin immunohistochemical staining (cohort A). Additionally, 27 pT1 CRCs without knowing VI status were selected for 3-dimensional (3D) VI evaluation only (cohort B). All 61 cases (cohorts A and B) were studied in 3D using tissue clearing. Results: VI was detected more commonly in 3D (17/34, 50.0%) than in 2D H&E slide evaluation (9/34, 26.5%, p = .047). When VI was identified in 3D (27/61, 44.3%), the most common phase was that of intraluminal growth (22/27, 81.5%), followed by intravasation (7/27, 25.9%) and extravasation (5/27, 18.5%). E-cadherin expression was characterized in 3D in foci of VI and varied in each phase of invasion. Conclusions: All three phases were observed in VI of pT1 CRCs. The extravasation of neoplastic cells from foci of VI in pT1 CRC suggests that VI could be a route of intratumoral spreading in a subset of pT1 CRCs.

Objective: We examined sociobehavioural factors associated with SARS-CoV-2 infection and estimated COVID-19 vaccine effectiveness against symptomatic SARS-CoV-2 infection in the Philippines. Such studies are limited in low- and middle-income countries, especially in Asia and the Pacific. Methods: A case-control study was conducted in two hospitals in Manila, Philippines, from March 2022 to June 2023. Sociobehavioural factors and vaccination history were collected. PCR-positive individuals were cases, while PCR-negative individuals were controls. Adjusted odds ratios (aORs) were calculated to examine associations between sociobehavioural factors/vaccination and medically attended SARS-CoV-2 infection. Results: The analysis included 2489 individuals (574 positive cases, 23.1%; 1915 controls, 76.9%; median age [interquartile range]: 35 [27–51] years). Although education and household income were not associated with infection, being a health-care worker was (aOR: 1.45; 95% confidence interval [CI]: 1.03–2.06). The odds of infection were higher among individuals who attended gatherings of five or more people compared to those who attended smaller gatherings (aOR: 2.58; 95% CI: 1.14–5.83). Absolute vaccine effectiveness for vaccination status was not estimated due to a high risk of bias, for example, unascertained prior infection. Moderate relative vaccine effectiveness for the first booster (32%; 95% CI: -120–79) and the second booster (48%; 95% CI: -23–78) were observed (both with wide CI), albeit with a waning trend after half a year. Discussion: The higher odds of infection among health-care workers emphasize the importance of infection prevention and control measures. Moderate relative vaccine effectiveness with a waning trend reiterates the need for more efficacious vaccines against symptomatic infection caused by circulating variants and with longer duration of protection.

roblem: Once COVID-19 vaccines were rolled out, there was a need to monitor real-world vaccine effectiveness to accumulate evidence to inform policy and risk communication. This was especially true in Japan and the Philippines, given historical issues that affected vaccine confidence. Context: Neither country had public health surveillance that could be enhanced to evaluate vaccine effectiveness or readily available national vaccination databases. Action: Study groups were established in multiple health-care facilities in each country to assess vaccine effectiveness against both symptomatic infection and severe disease. Outcome: In Japan, multiple study reports were published in Japanese on the website of the National Institute of Infectious Diseases and presented at the national government’s advisory board. Nationwide media coverage facilitated transparency and increased the confidence of the government and the public in the vaccination programme. In the Philippines, the launch of the study was delayed so as to align the research plan with the interests of various stakeholders and to obtain institutional review board approval. Ultimately, the studies were successfully initiated and completed. Discussion: There were four main challenges in conducting our studies: finding health-care facilities for data collection; obtaining exposure (vaccination) data; identifying epidemiological biases and confounders; and informing policy and risk communication in a timely manner. Preparedness during inter-emergency/epidemic/pandemic periods to rapidly evaluate relevant interventions such as vaccination is critical and should include the following considerations: (1) the establishment and maintenance of prospective data collection platforms, ideally under public health surveillance (if not, clinical research networks or linked databases); (2) uniform and practical protocols considering biases and confounders; and (3) communication with stakeholders including institutional review boards.

Objectives: Developing large language models (LLMs) in biomedicine requires access to high-quality training and alignment tuning datasets. However, publicly available Korean medical preference datasets are scarce, hindering the advancement of Korean medical LLMs. This study constructs and evaluates the efficacy of the Korean Medical Preference Dataset (KoMeP), an alignment tuning dataset constructed with an automated pipeline, minimizing the high costs of human annotation. Methods: KoMeP was generated using the DAHL score, an automated hallucination evaluation metric. Five LLMs (Dolly-v2-3B, MPT-7B, GPT-4o, Qwen-2-7B, Llama-3-8B) produced responses to 8,573 biomedical examination questions, from which 5,551 preference pairs were extracted. Each pair consisted of a “chosen” response and a “rejected” response, as determined by their DAHL scores. The dataset was evaluated when trained through two different alignment tuning methods, direct preference optimization (DPO) and odds ratio preference optimization (ORPO) respectively across five different models. The KorMedMCQA benchmark was employed to assess the effectiveness of alignment tuning. Results: Models trained with DPO consistently improved KorMedMCQA performance; notably, Llama-3.1-8B showed a 43.96% increase. In contrast, ORPO training produced inconsistent results. Additionally, English-to-Korean transfer learning proved effective, particularly for English-centric models like Gemma-2, whereas Korean-to-English transfer learning achieved limited success. Instruction tuning with KoMeP yielded mixed outcomes, which suggests challenges in dataset formatting. Conclusions KoMeP is the first publicly available Korean medical preference dataset and significantly improves alignment tuning performance in LLMs. The DPO method outperforms ORPO in alignment tuning. Future work should focus on expanding KoMeP, developing a Korean-native dataset, and refining alignment tuning methods to produce safer and more reliable Korean medical LLMs.

Objectives: Developing large language models (LLMs) in biomedicine requires access to high-quality training and alignment tuning datasets. However, publicly available Korean medical preference datasets are scarce, hindering the advancement of Korean medical LLMs. This study constructs and evaluates the efficacy of the Korean Medical Preference Dataset (KoMeP), an alignment tuning dataset constructed with an automated pipeline, minimizing the high costs of human annotation. Methods: KoMeP was generated using the DAHL score, an automated hallucination evaluation metric. Five LLMs (Dolly-v2-3B, MPT-7B, GPT-4o, Qwen-2-7B, Llama-3-8B) produced responses to 8,573 biomedical examination questions, from which 5,551 preference pairs were extracted. Each pair consisted of a “chosen” response and a “rejected” response, as determined by their DAHL scores. The dataset was evaluated when trained through two different alignment tuning methods, direct preference optimization (DPO) and odds ratio preference optimization (ORPO) respectively across five different models. The KorMedMCQA benchmark was employed to assess the effectiveness of alignment tuning. Results: Models trained with DPO consistently improved KorMedMCQA performance; notably, Llama-3.1-8B showed a 43.96% increase. In contrast, ORPO training produced inconsistent results. Additionally, English-to-Korean transfer learning proved effective, particularly for English-centric models like Gemma-2, whereas Korean-to-English transfer learning achieved limited success. Instruction tuning with KoMeP yielded mixed outcomes, which suggests challenges in dataset formatting. Conclusions KoMeP is the first publicly available Korean medical preference dataset and significantly improves alignment tuning performance in LLMs. The DPO method outperforms ORPO in alignment tuning. Future work should focus on expanding KoMeP, developing a Korean-native dataset, and refining alignment tuning methods to produce safer and more reliable Korean medical LLMs.

Objectives: Developing large language models (LLMs) in biomedicine requires access to high-quality training and alignment tuning datasets. However, publicly available Korean medical preference datasets are scarce, hindering the advancement of Korean medical LLMs. This study constructs and evaluates the efficacy of the Korean Medical Preference Dataset (KoMeP), an alignment tuning dataset constructed with an automated pipeline, minimizing the high costs of human annotation. Methods: KoMeP was generated using the DAHL score, an automated hallucination evaluation metric. Five LLMs (Dolly-v2-3B, MPT-7B, GPT-4o, Qwen-2-7B, Llama-3-8B) produced responses to 8,573 biomedical examination questions, from which 5,551 preference pairs were extracted. Each pair consisted of a “chosen” response and a “rejected” response, as determined by their DAHL scores. The dataset was evaluated when trained through two different alignment tuning methods, direct preference optimization (DPO) and odds ratio preference optimization (ORPO) respectively across five different models. The KorMedMCQA benchmark was employed to assess the effectiveness of alignment tuning. Results: Models trained with DPO consistently improved KorMedMCQA performance; notably, Llama-3.1-8B showed a 43.96% increase. In contrast, ORPO training produced inconsistent results. Additionally, English-to-Korean transfer learning proved effective, particularly for English-centric models like Gemma-2, whereas Korean-to-English transfer learning achieved limited success. Instruction tuning with KoMeP yielded mixed outcomes, which suggests challenges in dataset formatting. Conclusions KoMeP is the first publicly available Korean medical preference dataset and significantly improves alignment tuning performance in LLMs. The DPO method outperforms ORPO in alignment tuning. Future work should focus on expanding KoMeP, developing a Korean-native dataset, and refining alignment tuning methods to produce safer and more reliable Korean medical LLMs.