The field of onco-microbiome is rapidly expanding. Multiple studies have shown the crucial role of gut microbiota in the regulation of nutrient metabolism, immunomodulation and protection against pathogens. Tools for manipulating the gut microbiota include dietary modification and faecal microbiota transfer. Accumulating evidence has also documented the application of specific intestinal microbiome in cancer immunotherapy, notably in enhancing the efficacy of immune checkpoint inhibitors. The aim of this review is to focus on the East Asian microbiome and to provide a current overview of microbiome science and its clinical application in cancer biology and immunotherapy.
Humans ; Gastrointestinal Microbiome ; Neoplasms/microbiology* ; Immunotherapy/methods* ; Asia, Eastern ; Medical Oncology ; Fecal Microbiota Transplantation ; Immune Checkpoint Inhibitors/therapeutic use* ; East Asian People

INTRODUCTION: We aimed to understand the awareness and attitudes of elderly Southeast Asians towards telehealth services during the coronavirus disease 2019 (COVID-19) pandemic in this study. METHODS: In this qualitative study, 78 individuals from Singapore (51.3% female, mean age 73.0 ± 7.6 years) were interviewed via telephone between 13 May 2020 and 9 June 2020 during Singapore's first COVID-19 'circuit breaker'. Participants were asked to describe their understanding of telehealth, their experience of and willingness to utilise these services, and the barriers and facilitators underlying their decision. Transcripts were analysed using thematic analysis, guided by the United Theory of Acceptance Use of Technology framework. RESULTS: Of the 78 participants, 24 (30.8%) were able to describe the range of telehealth services available and 15 (19.2%) had previously utilised these services. Conversely, 14 (17.9%) participants thought that telehealth comprised solely home medication delivery and 50 (51.3%) participants did not know about telehealth. Despite the advantages offered by telehealth services, participants preferred in-person consultations due to a perceived lack of human interaction and accuracy of diagnoses, poor digital literacy and a lack of access to telehealth-capable devices. CONCLUSION Our results showed poor overall awareness of the range of telehealth services available among elderly Asian individuals, with many harbouring erroneous views regarding their use. These data suggest that public health education campaigns are needed to improve awareness of and correct negative perceptions towards telehealth services in elderly Asians.
Humans ; COVID-19/epidemiology* ; Female ; Telemedicine ; Aged ; Male ; Singapore/epidemiology* ; Qualitative Research ; Health Knowledge, Attitudes, Practice ; SARS-CoV-2 ; Aged, 80 and over ; Middle Aged ; Pandemics ; Awareness ; Asian People ; Southeast Asian People

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.

Background/Aims: The performance of machine learning (ML) in predicting the outcomes of patients with hepatocellular carcinoma (HCC) remains uncertain. We aimed to develop risk scores using conventional methods and ML to categorize early-stage HCC patients into distinct prognostic groups. Methods: The study retrospectively enrolled 1,411 consecutive treatment-naïve patients with the Barcelona Clinic Liver Cancer (BCLC) stage 0 to A HCC from 2012 to 2021. The patients were randomly divided into a training cohort (n=988) and validation cohort (n=423). Two risk scores (CATS-IF and CATS-INF) were developed to predict overall survival (OS) in the training cohort using the conventional methods (Cox proportional hazards model) and ML-based methods (LASSO Cox regression), respectively. They were then validated and compared in the validation cohort. Results: In the training cohort, factors for the CATS-IF score were selected by the conventional method, including age, curative treatment, single large HCC, serum creatinine and alpha-fetoprotein levels, fibrosis-4 score, lymphocyte-tomonocyte ratio, and albumin-bilirubin grade. The CATS-INF score, determined by ML-based methods, included the above factors and two additional ones (aspartate aminotransferase and prognostic nutritional index). In the validation cohort, both CATS-IF score and CATS-INF score outperformed other modern prognostic scores in predicting OS, with the CATSINF score having the lowest Akaike information criterion value. A calibration plot exhibited good correlation between predicted and observed outcomes for both scores. Conclusions Both the conventional Cox-based CATS-IF score and ML-based CATS-INF score effectively stratified patients with early-stage HCC into distinct prognostic groups, with the CATS-INF score showing slightly superior performance.

Objective: Anxious depression is a prevalent characteristic observed in Asian psychiatric patients diagnosed with major depressive disorder (MDD). This study aims to investigate the prevalence and clinical presentation of anxious depression in Taiwanese individuals diagnosed with MDD. Methods: We recruited psychiatric outpatients aged over 18 who had been diagnosed with MDD through clinical interviews. This recruitment took place at five hospitals located in northern Taiwan. We gathered baseline clinical and demographic information from the participants. Anxious depression was identified using a threshold of an anxiety/somatization factor score ≥7 on the 21-item Hamilton Rating Scale for Depression (HAM-D). Results: In our study of 399 patients (84.21% female), 64.16% met the criteria for anxious depression. They tended to be older, married, less educated, with more children, and an older age of onset. Anxious depression patients had higher HAM-D and Clinical Global Impression–Severity scale score, more panic disorder (without agoraphobia), and exhibited symptoms like agitation, irritability, concentration difficulties, psychological and somatic anxiety, somatic complaints, hypochondriasis, weight loss, and increased insight. Surprisingly, their suicide rates did not significantly differ from non-anxious depression patients. This highlights the importance of recognizing and addressing these unique characteristics. Conclusion Our study findings unveiled that the prevalence of anxious depression among Taiwanese outpatients diagnosed with MDD was lower compared to inpatients but substantially higher than the reported rates in European countries and the United States. Furthermore, patients with anxious depression exhibited a greater occurrence of somatic symptoms.

With the advent of artificial intelligence (AI), machines are increasingly being used to complete complicated tasks, yielding remarkable results. Machine learning (ML) is the most relevant subset of AI in medicine, which will soon become an integral part of our everyday practice. Therefore, physicians should acquaint themselves with ML and AI, and their role as an enabler rather than a competitor. Herein, we introduce basic concepts and terms used in AI and ML, and aim to demystify commonly used AI/ML algorithms such as learning methods including neural networks/deep learning, decision tree and application domain in computer vision and natural language processing through specific examples. We discuss how machines are already being used to augment the physician's decision-making process, and postulate the potential impact of ML on medical practice and medical research based on its current capabilities and known limitations. Moreover, we discuss the feasibility of full machine autonomy in medicine.
Humans ; Artificial Intelligence ; Machine Learning ; Algorithms ; Neural Networks, Computer ; Medicine

Summary Sporotrichosis is a rare and chronic granulomatous subcutaneous mycotic infection caused by a dimorphic fungus, Sporothrix schenckii. We describe a patient with disseminated cutaneous sporotrichosis who was later diagnosed with myeloproliferative neoplasm and discuss the challenges and importance in diagnosing this rare condition.
Sporotrichosis ; Granulomatous Disease, Chronic ; Myeloproliferative Disorders

Objective: This study aimed to determine the effect of radiotherapy (RT) on the risk of herpes zoster (HZ) in patients with gynecological cancers via a nationwide population-based study. Methods: Based on patient data obtained from the National Health Insurance Research Database, 1928 gynecological cancer patients were identified with 1:1 matching for RT and non-RT cohorts by age, index date, and cancer type. Another cohort consisting of 964 noncancer individuals matched was used as normal control. The incidence of HZ was compared between cancer patients with and without RT. Age, comorbidities, cancer-related surgery and chemotherapy (CT), and cancer type were adjusted as confounders. Results: The risk of HZ in cancer patients was higher than that of non-cancer individuals (14.23 versus 8.34 per 1,000 person-years [PY], the adjusted hazard ratio [aHR]=1.38, p=0.044). In the cancer population, the incidence of HZ for the RT and non-RT cohorts was 20.55 versus 10.23 per 1,000 PY, respectively (aHR=1.68, p=0.009). Age >50 years was an independent factor for developing HZ. The 5-year actuarial incidence for patients receiving neither RT nor CT, RT alone, CT alone, and combined modalities was 5.4%, 6.9%, 3.7%, and 9.9%, respectively (p<0.001). In the RT cohort, the risk rose rapidly in the first year, becoming steady thereafter. Conclusion This population-based study showed that gynecological cancer patients receiving RT combined with CT had the highest cumulative risk of HZ. Health care professionals should be aware of the potential toxicities.