Language patterns in Japanese patients with Alzheimer disease: A machine learning approach.

AIM: The authors applied natural language processing and machine learning to explore the disease-related language patterns that warrant objective measures for assessing language ability in Japanese patients with Alzheimer disease (AD), while most previous studies have used large publicly available data sets in Euro-American languages. METHODS: The authors obtained 276 speech samples from 42 patients with AD and 52 healthy controls, aged 50 years or older. A natural language processing library for Python was used, spaCy, with an add-on library, GiNZA, which is a Japanese parser based on Universal Dependencies designed to facilitate multilingual parser development. The authors used eXtreme Gradient Boosting for our classification algorithm. Each unit of part-of-speech and dependency was tagged and counted to create features such as tag-frequency and tag-to-tag transition-frequency. Each feature's importance was computed during the 100-fold repeated random subsampling validation and averaged. RESULTS: The model resulted in an accuracy of 0.84 (SD = 0.06), and an area under the curve of 0.90 (SD = 0.03). Among the features that were important for such predictions, seven of the top 10 features were related to part-of-speech, while the remaining three were related to dependency. A box plot analysis demonstrated that the appearance rates of content words-related features were lower among the patients, whereas those with stagnation-related features were higher. CONCLUSION: The current study demonstrated a promising level of accuracy for predicting AD and found the language patterns corresponding to the type of lexical-semantic decline known as 'empty speech', which is regarded as a characteristic of AD.

The core autophagy protein ATG5 controls the polarity of the Golgi apparatus and insulin secretion of pancreatic beta cells.

Pancreatic beta cells are insulin-producing cells that are structurally and functionally polarized in the islets of Langerhans. The organization and position of the Golgi complex play a key role in maintaining a polarized cell state, but the factors and molecular mechanisms determining the Golgi polarization of pancreatic beta cells are still unknown. In the current study, using pancreatic beta cell-specific Atg5 knockout mice, we found that Atg5, an essential gene for autophagy, plays a pivotal role in regulating Golgi integrity and polarization by affecting the expression of genes involved in vesicle transport. Deletion of Atg5 led to endoplasmic reticulum (ER) stress and impaired the distribution of proinsulin and insulin secretion of pancreatic beta cells, which further exacerbates diabetes. These results contribute to a comprehensive understanding of autophagy-mediated Golgi polarization and its regulation of the function of pancreatic beta cells.

MetaVelvet-DL: a MetaVelvet deep learning extension for de novo metagenome assembly.

BACKGROUND: The increasing use of whole metagenome sequencing has spurred the need to improve de novo assemblers to facilitate the discovery of unknown species and the analysis of their genomic functions. MetaVelvet-SL is a short-read de novo metagenome assembler that partitions a multi-species de Bruijn graph into single-species sub-graphs. This study aimed to improve the performance of MetaVelvet-SL by using a deep learning-based model to predict the partition nodes in a multi-species de Bruijn graph. RESULTS: This study showed that the recent advances in deep learning offer the opportunity to better exploit sequence information and differentiate genomes of different species in a metagenomic sample. We developed an extension to MetaVelvet-SL, which we named MetaVelvet-DL, that builds an end-to-end architecture using Convolutional Neural Network and Long Short-Term Memory units. The deep learning model in MetaVelvet-DL can more accurately predict how to partition a de Bruijn graph than the Support Vector Machine-based model in MetaVelvet-SL can. Assembly of the Critical Assessment of Metagenome Interpretation (CAMI) dataset showed that after removing chimeric assemblies, MetaVelvet-DL produced longer single-species contigs, with less misassembled contigs than MetaVelvet-SL did. CONCLUSIONS: MetaVelvet-DL provides more accurate de novo assemblies of whole metagenome data. The authors believe that this improvement can help in furthering the understanding of microbiomes by providing a more accurate description of the metagenomic samples under analysis.

Evaluating the severity of depressive symptoms using upper body motion captured by RGB-depth sensors and machine learning in a clinical interview setting: A preliminary study.

BACKGROUND: Mood disorders have long been known to affect motor function. While methods to objectively assess such symptoms have been used in experiments, those same methods have not yet been applied in clinical practice because the methods are time-consuming, labor-intensive, or invasive. METHODS: We videotaped the upper body of each subject using a Red-Green-Blue-Depth (RGB-D) sensor during a clinical interview setting. We then examined the relationship between depressive symptoms and body motion by comparing the head motion of patients with major depressive disorders (MDD) and bipolar disorders (BD) to the motion of healthy controls (HC). Furthermore, we attempted to predict the severity of depressive symptoms by using machine learning. RESULTS: A total of 47 participants (HC, n = 16; MDD, n = 17; BD, n = 14) participated in the study, contributing to 144 data sets. It was found that patients with depression move significantly slower compared to HC in the 5th percentile and 50th percentile of motion speed. In addition, Hamilton Depression Rating Scale (HAMD)-17 scores correlated with 5th percentile, 50th percentile, and mean speed of motion. Moreover, using machine learning, the presence and/or severity of depressive symptoms based on HAMD-17 scores were distinguished by a kappa coefficient of 0.37 to 0.43. LIMITATIONS: Limitations include the small number of subjects, especially the number of severe cases and young people. CONCLUSIONS: The RGB-D sensor captured some differences in upper body motion between depressed patients and controls. If much larger samples are accumulated, machine learning may be useful in identifying objective measures for depression in the future.

Speech Quality Feature Analysis for Classification of Depression and Dementia Patients.

Loss of cognitive ability is commonly associated with dementia, a broad category of progressive brain diseases. However, major depressive disorder may also cause temporary deterioration of one's cognition known as pseudodementia. Differentiating a true dementia and pseudodementia is still difficult even for an experienced clinician and extensive and careful examinations must be performed. Although mental disorders such as depression and dementia have been studied, there is still no solution for shorter and undemanding pseudodementia screening. This study inspects the distribution and statistical characteristics from both dementia patient and depression patient, and compared them. It is found that some acoustic features were shared in both dementia and depression, albeit their correlation was reversed. Statistical significance was also found when comparing the features. Additionally, the possibility of utilizing machine learning for automatic pseudodementia screening was explored. The machine learning part includes feature selection using LASSO algorithm and support vector machine (SVM) with linear kernel as the predictive model with age-matched symptomatic depression patient and dementia patient as the database. High accuracy, sensitivity, and specificity was obtained in both training session and testing session. The resulting model was also tested against other datasets that were not included and still performs considerably well. These results imply that dementia and depression might be both detected and differentiated based on acoustic features alone. Automated screening is also possible based on the high accuracy of machine learning results.

Predicting Individual Remission After Electroconvulsive Therapy Based on Structural Magnetic Resonance Imaging: A Machine Learning Approach.

OBJECTIVE: To identify important clinical or imaging features predictive of an individual's response to electroconvulsive therapy (ECT) by utilizing a machine learning approach. METHODS: Twenty-seven depressed patients who received ECT were recruited. Clinical demographics and pretreatment structural magnetic resonance imaging (MRI) data were used as candidate features to build models to predict remission and post-ECT Hamilton Depression Rating Scale scores. Support vector machine and support vector regression with elastic-net regularization were used to build models using (i) only clinical features, (ii) only MRI features, and (iii) both clinical and MRI features. Consistently selected features across all individuals were identified through leave-one-out cross-validation. RESULTS: Compared with models that include only clinical variables, the models including MRI data improved the prediction of ECT remission: the prediction accuracy improved from 70% to 93%. Features selected consistently across all individuals included volumes in the gyrus rectus, the right anterior lateral temporal lobe, the cuneus, and the third ventricle, as well as 2 clinical features: psychotic features and family history of mood disorder. CONCLUSIONS: Pretreatment structural MRI data improved the individual predictive accuracy of ECT remission, and only a small subset of features was important for prediction.

Etoposide-induced protein 2.4 functions as a regulator of the calcium ATPase and protects pancreatic ß-cell survival.

Calcium homeostasis is essential for maintaining the viability and function of pancreatic ß cells and plays a key role in preventing the development of diabetes. Decreased levels of ATPase sarcoplasmic/endoplasmic reticulum Ca2+-transporting 2 (ATP2a2), the main calcium pump in ß cells, are often found in individuals with diabetes and in diabetic animal models. However, the regulators of ATP2a2 and the molecular mechanisms responsible for controlling ATP2a2 activity remain unclear. Etoposide-induced protein 2.4 (Ei24) is also down-regulated in ß cells of diabetic individuals, whereas the effect of decreased Ei24 level on ß-cell function is not clarified. Here, using Cre-LoxP and CRISPR/Cas9-based genomic knockout (KO) approaches to generate pancreatic ß cell-specific Ei24 KO mice and pancreatic ß-cell lines, we found that Ei24 regulates ATP2a2 activity. Specifically, we observed that Ei24 binds to ATP2a2 through Ei24 residues 293-299, which we named here the ATP2a2-interacting region (AIR). Loss of Ei24 inactivated ATP2a2, disrupted calcium homeostasis, and deactivated the calcium/calmodulin-dependent protein kinase kinase 2 (CAMKK2)-AMP-activated protein kinase (AMPK) pathway. Elevation of calcium concentration in the endoplasmic reticulum or agonist-induced AMPK activation rescued pancreatic ß-cell survival and improved glucose tolerance of Ei24 KO mice. Our findings indicate that targeting the Ei24-ATP2a2 interaction to increase ATP2a2 activity can protect pancreatic ß cells and improve glucose homeostasis in diabetic models, suggesting that Ei24 could potentially serve as a target to prevent or manage diabetes.

Effect of electroconvulsive therapy on hippocampal and amygdala volumes: systematic review and meta-analysis.

BACKGROUND: Electroconvulsive therapy (ECT) is one of the most effective treatments for depression, although the underlying mechanisms remain unclear. Animal studies have shown that electroconvulsive shock induced neuroplastic changes in the hippocampus. Aims To summarise volumetric magnetic resonance imaging studies investigating the effects of ECT on limbic brain structures. METHOD: A systematic review and meta-analysis was conducted to assess volumetric changes of each side of the hippocampus and amygdala before and after ECT. Standardised mean difference (SMD) was calculated. RESULTS: A total of 8 studies (n = 193) were selected for our analyses. Both right and left hippocampal and amygdala volumes increased after ECT. Meta-regression analyses revealed that age, percentage of those responding and percentage of those in remission were negatively associated with volume increases in the left hippocampus. CONCLUSIONS: ECT increased brain volume in the limbic structures. The clinical relevance of volume increase needs further investigation. Declaration of interest None.

Genome-wide analysis reveals NRP1 as a direct HIF1α-E2F7 target in the regulation of motorneuron guidance in vivo.

In this study, we explored the existence of a transcriptional network co-regulated by E2F7 and HIF1α, as we show that expression of E2F7, like HIF1α, is induced in hypoxia, and because of the previously reported ability of E2F7 to interact with HIF1α. Our genome-wide analysis uncovers a transcriptional network that is directly controlled by HIF1α and E2F7, and demonstrates both stimulatory and repressive functions of the HIF1α -E2F7 complex. Among this network we reveal Neuropilin 1 (NRP1) as a HIF1α-E2F7 repressed gene. By performing in vitro and in vivo reporter assays we demonstrate that the HIF1α-E2F7 mediated NRP1 repression depends on a 41 base pairs 'E2F-binding site hub', providing a molecular mechanism for a previously unanticipated role for HIF1α in transcriptional repression. To explore the biological significance of this regulation we performed in situ hybridizations and observed enhanced nrp1a expression in spinal motorneurons (MN) of zebrafish embryos, upon morpholino-inhibition of e2f7/8 or hif1α Consistent with the chemo-repellent role of nrp1a, morpholino-inhibition of e2f7/8 or hif1α caused MN truncations, which was rescued in TALEN-induced nrp1a(hu10012) mutants, and phenocopied in e2f7/8 mutant zebrafish. Therefore, we conclude that repression of NRP1 by the HIF1α-E2F7 complex regulates MN axon guidance in vivo.

Utilization of Facial Image Analysis Technology for Blink Detection: A Validation Study.

PURPOSE: The assessment of anterior eye diseases and the understanding of psychological functions of blinking can benefit greatly from a validated blinking detection technology. In this work, we proposed an algorithm based on facial recognition built on current video processing technologies to automatically filter and analyze blinking movements. We compared electrooculography (EOG), the gold standard of blinking measurement, with manual video tape recording counting (mVTRc) and our proposed automated video tape recording analysis (aVTRa) in both static and dynamic conditions to validate our aVTRa method. METHODS: We measured blinking in both static condition, where the subject was sitting still with chin fixed on the table, and dynamic condition, where the subject's face was not fixed and natural communication was taking place between the subject and interviewer. We defined concordance of blinks between measurement methods as having less than 50 ms difference between eyes opening and closing. RESULTS: The subjects consisted of seven healthy Japanese volunteers (3 male, four female) without significant eye disease with average age of 31.4±7.2. The concordance of EOG vs. aVTRa, EOG vs. mVTRc, and aVTRa vs. mVTRc (average±SD) were found to be 92.2±10.8%, 85.0±16.5%, and 99.6±1.0% in static conditions and 32.6±31.0%, 28.0±24.2%, and 98.5±2.7% in dynamic conditions, respectively. CONCLUSIONS: In static conditions, we have found a high blink concordance rate between the proposed aVTRa versus EOG, and confirmed the validity of aVTRa in both static and dynamic conditions.

E2F7 and E2F8 promote angiogenesis through transcriptional activation of VEGFA in cooperation with HIF1.

The E2F family of transcription factors plays an important role in controlling cell-cycle progression. While this is their best-known function, we report here novel functions for the newest members of the E2F family, E2F7 and E2F8 (E2F7/8). We show that simultaneous deletion of E2F7/8 in zebrafish and mice leads to severe vascular defects during embryonic development. Using a panel of transgenic zebrafish with fluorescent-labelled blood vessels, we demonstrate that E2F7/8 are essential for proper formation of blood vessels. Despite their classification as transcriptional repressors, we provide evidence for a molecular mechanism through which E2F7/8 activate the transcription of the vascular endothelial growth factor A (VEGFA), a key factor in guiding angiogenesis. We show that E2F7/8 directly bind and stimulate the VEGFA promoter independent of canonical E2F binding elements. Instead, E2F7/8 form a transcriptional complex with the hypoxia inducible factor 1 (HIF1) to stimulate VEGFA promoter activity. These results uncover an unexpected link between E2F7/8 and the HIF1-VEGFA pathway providing a molecular mechanism by which E2F7/8 control angiogenesis.

Linking transcriptional changes over time in stimulated dendritic cells to identify gene networks activated during the innate immune response.

The innate immune response is primarily mediated by the Toll-like receptors functioning through the MyD88-dependent and TRIF-dependent pathways. Despite being widely studied, it is not yet completely understood and systems-level analyses have been lacking. In this study, we identified a high-probability network of genes activated during the innate immune response using a novel approach to analyze time-course gene expression profiles of activated immune cells in combination with a large gene regulatory and protein-protein interaction network. We classified the immune response into three consecutive time-dependent stages and identified the most probable paths between genes showing a significant change in expression at each stage. The resultant network contained several novel and known regulators of the innate immune response, many of which did not show any observable change in expression at the sampled time points. The response network shows the dominance of genes from specific functional classes during different stages of the immune response. It also suggests a role for the protein phosphatase 2a catalytic subunit α in the regulation of the immunoproteasome during the late phase of the response. In order to clarify the differences between the MyD88-dependent and TRIF-dependent pathways in the innate immune response, time-course gene expression profiles from MyD88-knockout and TRIF-knockout dendritic cells were analyzed. Their response networks suggest the dominance of the MyD88-dependent pathway in the innate immune response, and an association of the circadian regulators and immunoproteasomal degradation with the TRIF-dependent pathway. The response network presented here provides the most probable associations between genes expressed in the early and the late phases of the innate immune response, while taking into account the intermediate regulators. We propose that the method described here can also be used in the identification of time-dependent gene sub-networks in other biological systems.

Left coronary sinus of valsalva aneurysm dissecting into interventricular septum: a case report.

BACKGROUND: Sinus of Valsalva aneurysm (SVA) is an extremely rare condition, and its rupture causes acute symptoms such as chest pain and dyspnea. Ruptured SVA is frequently associated with other congenital defects. CASE PRESENTATION: A 37-year-old male presented with SVA originating from the left coronary sinus that ruptured into the interventricular septum. SVA was diagnosed by echocardiography, cardiac computed tomography and magnetic resonance imaging, and confirmed during the operation. CONCLUSIONS: SVA is a rare cardiac abnormality which can lead to severe clinical symptoms upon rupture. Immediate surgery is necessary to repair the ruptured SVA.

Lenvatinib­based treatment regimens in conversion therapy of unresectable hepatocellular carcinoma: A systematic review and meta­analysis.

Hepatocellular carcinoma (HCC) is a malignancy associated with high morbidity and mortality rates. Conversion therapy provides patients with unresectable HCC (uHCC) the opportunity to undergo radical treatment and achieve long-term survival. Despite accumulating evidence regarding the efficacy of conversion therapy, the optimal treatment approach for such therapy remains uncertain. Lenvatinib (LEN) has shown efficacy and tolerable rates of adverse events (AEs) when applied in combination with immune checkpoint inhibitors (ICIs) or locoregional therapy (LRT) over the past decade. Therefore, the present meta-analysis was performed to systematically assess the safety and efficacy of LEN-based treatment regimens in conversion therapies for uHCC. Data on outcomes, including the conversion rate, objective response rate (ORR), disease control rate (DCR) and AE incidence in patients with uHCC, were collected. A systematic literature search was performed using MEDLINE, Embase, Web of Science and Cochrane Library databases, up to the date of September 1, 2023. In total, 16 studies, encompassing a total of 1,650 cases of uHCC, were included in the final meta-analysis. The pooled conversion rates for LEN alone, LEN + ICI, LEN + LRT and LEN + ICI + LRT were calculated to be 0.04 (95% CI, 0.00-0.07; I2=77%), 0.23 (95% CI, 0.16-0.30; I2=66%), 0.14 (95% CI, 0.10-0.18; I2=0%) and 0.35 (95% CI, 0.23-0.47; I2=88%), respectively. The pooled ORRs for LEN alone, LEN + ICI, LEN + LRT and LEN + ICI + LRT were found to be 0.45 (95% CI, 0.23-0.67; I2=96%), 0.49 (95% CI, 0.39-0.60; I2=78%), 0.43 (95% CI, 0.24-0.62; I2=88%) and 0.69 (95% CI, 0.56-0.82; I2=92%), respectively. The pooled DCRs for LEN alone, LEN + ICI, LEN + LRT and LEN + ICI + LRT were observed to be 0.77 (95% CI, 0.73-0.81; I2=23%), 0.82 (95% CI, 0.69-0.95; I2=90%), 0.67 (95% CI, 0.39-0.94; I2=94%) and 0.87 (95% CI, 0.82-0.93; I2=67%), respectively. The pooled grade ≥3 AEs for LEN alone, LEN + ICI, LEN + LRT and LEN + ICI + LRT were 0.25 (95% CI, 0.14-0.36; I2=89%), 0.43 (95% CI, 0.34-0.53; I2=23%), 0.42 (95% CI, 0.19-0.66; I2=81%) and 0.35 (95% CI, 0.17-0.54; I2=94%), respectively. These findings suggested that LEN-based combination strategies may confer efficacy and acceptable tolerability for patients with uHCC. In particular, LEN + ICI, with or without LRT, appears to represent a highly effective conversion regimen, with an acceptable conversion rate and well-characterized safety profile.

Development of in situ forming thermosensitive hydrogel for radiotherapy combined with chemotherapy in a mouse model of hepatocellular carcinoma.

This study evaluated a system for local cancer radiotherapy combined with chemotherapy. The delivery system is a thermosensitive hydrogel containing a therapeutic radionuclide ((188)Re-Tin colloid) and a chemotherapeutic drug (liposomal doxorubicin). The thermosensitive PCL-PEG-PCL copolymer was designed to spontaneously undergo a sol-gel phase transition in response to temperature, remaining liquid at room temperature and rapidly forming a gel at body temperature. A scanning electron microscope was used to observe the microstructure of the fully loaded hydrogel. Release of radionuclide and doxorubicin from the hydrogel was slow, and the system tended to remain stable for at least 10 days. After the intratumoral administration of Lipo-Dox/(188)Re-Tin hydrogel in mice with hepatocellular carcinoma (HCC), its retention by the tumor, spatiotemporal distribution, and therapeutic effect were evaluated. The residence time in the tumor was significantly longer for (188)Re-Tin loaded hydrogel than for Na (188)Re perrhenate (Na (188)ReO4). The hydrogel after thermal transition kept the radionuclide inside the tumor, whereas free (188)Re perrhenate ((188)ReO4) diffused quickly from the tumor. The tumor growth was more profoundly inhibited by treatment with Lipo-Dox/(188)Re-Tin hydrogel (with up to 80% regression of well-established tumors on day 32) than treatment with either (188)Re-Tin hydrogel or Lipo-Dox hydrogel. Therefore, this injectable and biodegradable hydrogel may offer the advantage of focusing radiotherapy and chemotherapy locally to maximize their effects on hepatocellular carcinoma.

Association of sarcopenia with liver fibrosis and steatohepatitis in non-alcoholic fatty liver disease: protocol for a systematic review and meta-analysis.

INTRODUCTION: Non-alcoholic fatty liver disease (NAFLD) has become the most common chronic liver disorder over the last four decades, more evidence shows a high prevalence of sarcopenia in NAFLD that may influence disease severity. This meta-analysis aims to determine the association of sarcopenia with liver fibrosis and steatohepatitis in NAFLD. METHODS AND ANALYSIS: We will conduct the literature search using Medline (via PubMed), Web of Science databases, EMBASE, Cochrane Central Register of Controlled Trials and the Cochrane Database of Systematic Reviews (from the date of inception to 1 May 2022). There will be no restriction to the publication year. Two reviewers will independently screen the articles and abstract key study characteristics. The outcome of this meta-analysis is the strength of association of sarcopenia with liver fibrosis and steatohepatitis in NAFLD. The STATA (V.14, StataCorp, 2015) will be used to carry out the statistical analysis. Comprehensive evaluation of bias risk and heterogeneity will be performed before data synthesis. Also, consistency and evidence quality will be assessed. ETHICS AND DISSEMINATION: There will be no need of ethics approval as this systematic review is summary and analysis of existing literature. Final results may be presented in international conferences or a peer-reviewed journal. PROSPERO REGISTRATION NUMBER: CRD42022322685.

Contribution of different mechanisms to pancreatic beta-cell hyper-secretion in non-obese diabetic (NOD) mice during pre-diabetes.

The development of insulin-dependent diabetes mellitus (IDDM) results from the selective destruction of pancreatic beta-cells. Both humans and spontaneous models of IDDM, such as NOD mice, have an extended pre-diabetic stage. Dynamic changes in beta-cell mass and function during pre-diabetes, such as insulin hyper-secretion, remain largely unknown. In this paper, we evaluated pre-diabetic female NOD mice at different ages (6, 10, and 14 weeks old) to illustrate alterations in beta-cell mass and function as disease progressed. We found an increase in beta-cell mass in 6-week-old NOD mice that may account for improved glucose tolerance in these mice. As NOD mice aged, beta-cell mass progressively reduced with increasing insulitis. In parallel, secretory ability of individual beta-cells was enhanced due to an increase in the size of slowly releasable pool (SRP) of vesicles. Moreover, expression of both SERCA2 and SERCA3 genes were progressively down-regulated, which facilitated depolarization-evoked secretion by prolonging Ca(2+) elevation upon glucose stimulation. In summary, we propose that different mechanisms contribute to the insulin hyper-secretion at different ages of pre-diabetic NOD mice, which may provide some new ideas concerning the progression and management of type I diabetes.

Clinical prognosis of intraoperative blood salvage autotransfusion in liver transplantation for hepatocellular carcinoma: A systematic review and meta-analysis.

Background: Intraoperative blood salvage autotransfusion(IBSA) has been widely used in a variety of surgeries, but the use of IBSA in hepatocellular carcinoma (HCC) patients undergoing liver transplantation (LT) is controversial. Numerous studies have reported that IBSA used during LT for HCC is not associated with adverse oncologic outcomes. This systematic review and meta-analysis aims to estimate the clinical prognosis of IBSA for patients with H+CC undergoing LT. Methods: MEDLINE, Embase, Web of Science, and Cochrane Library were searched for articles describing IBSA in HCC patients undergoing LT from the date of inception until May 1, 2022, and a meta-analysis was performed. Study heterogeneity was assessed by I2 test. Publication bias was evaluated by funnel plots, Egger's and Begg's test. Results: 12 studies enrolling a total of 2253 cases (1374 IBSA and 879 non-IBSA cases) are included in this meta-analysis. The recurrence rate(RR) at 5-year(OR=0.75; 95%CI, 0.59-0.95; P=0.02) and 7-year(OR=0.65; 95%CI, 0.55-0.97; P=0.03) in the IBSA group is slightly lower than non-IBSA group. There are no significant differences in the 1-year RR(OR=0.77; 95% CI, 0.56-1.06; P=0.10), 3-years RR (OR=0.79; 95% CI, 0.62-1.01; P=0.06),1-year overall survival outcome(OS) (OR=0.90; 95% CI, 0.63-1.28; P=0.57), 3-year OS(OR=1.16; 95% CI, 0.83-1.62; P=0.38), 5-year OS(OR=1.04; 95% CI, 0.76-1.40; P=0.82),1-year disease-free survival rate(DFS) (OR=0.80; 95%CI, 0.49-1.30; P=0.36), 3-year DFS(OR=0.99; 95%CI, 0.64-1.55; P=0.98), and 5-year DFS(OR=0.88; 95%CI, 0.60-1.28; P=0.50). Subgroup analysis shows a difference in the use of leukocyte depletion filters group of 5-year RR(OR=0.73; 95%CI, 0.55-0.96; P=0.03). No significant differences are found in other subgroups. Conclusions: IBSA provides comparable survival outcomes relative to allogeneic blood transfusion and does not increase the tumor recurrence for HCC patients after LT. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42022295479.

Understanding psychiatric illness through natural language processing (UNDERPIN): Rationale, design, and methodology.

Introduction: Psychiatric disorders are diagnosed through observations of psychiatrists according to diagnostic criteria such as the DSM-5. Such observations, however, are mainly based on each psychiatrist's level of experience and often lack objectivity, potentially leading to disagreements among psychiatrists. In contrast, specific linguistic features can be observed in some psychiatric disorders, such as a loosening of associations in schizophrenia. Some studies explored biomarkers, but biomarkers have yet to be used in clinical practice. Aim: The purposes of this study are to create a large dataset of Japanese speech data labeled with detailed information on psychiatric disorders and neurocognitive disorders to quantify the linguistic features of those disorders using natural language processing and, finally, to develop objective and easy-to-use biomarkers for diagnosing and assessing the severity of them. Methods: This study will have a multi-center prospective design. The DSM-5 or ICD-11 criteria for major depressive disorder, bipolar disorder, schizophrenia, and anxiety disorder and for major and minor neurocognitive disorders will be regarded as the inclusion criteria for the psychiatric disorder samples. For the healthy subjects, the absence of a history of psychiatric disorders will be confirmed using the Mini-International Neuropsychiatric Interview (M.I.N.I.). The absence of current cognitive decline will be confirmed using the Mini-Mental State Examination (MMSE). A psychiatrist or psychologist will conduct 30-to-60-min interviews with each participant; these interviews will include free conversation, picture-description task, and story-telling task, all of which will be recorded using a microphone headset. In addition, the severity of disorders will be assessed using clinical rating scales. Data will be collected from each participant at least twice during the study period and up to a maximum of five times at an interval of at least one month. Discussion: This study is unique in its large sample size and the novelty of its method, and has potential for applications in many fields. We have some challenges regarding inter-rater reliability and the linguistic peculiarities of Japanese. As of September 2022, we have collected a total of >1000 records from >400 participants. To the best of our knowledge, this data sample is one of the largest in this field. Clinical Trial Registration: Identifier: UMIN000032141.

Development of medical device software for the screening and assessment of depression severity using data collected from a wristband-type wearable device: SWIFT study protocol.

Introduction: Few biomarkers can be used clinically to diagnose and assess the severity of depression. However, a decrease in activity and sleep efficiency can be observed in depressed patients, and recent technological developments have made it possible to measure these changes. In addition, physiological changes, such as heart rate variability, can be used to distinguish depressed patients from normal persons; these parameters can be used to improve diagnostic accuracy. The proposed research will explore and construct machine learning models capable of detecting depressive episodes and assessing their severity using data collected from wristband-type wearable devices. Methods and analysis: Patients with depressive symptoms and healthy subjects will wear a wristband-type wearable device for 7 days; data on triaxial acceleration, pulse rate, skin temperature, and ultraviolet light will be collected. On the seventh day of wearing, the severity of depressive episodes will be assessed using Structured Clinical Interview for DSM-5 (SCID-5), Hamilton Depression Rating Scale (HAMD), and other scales. Data for up to five 7-day periods of device wearing will be collected from each subject. Using wearable device data associated with clinical symptoms as supervisory data, we will explore and build a machine learning model capable of identifying the presence or absence of depressive episodes and predicting the HAMD scores for an unknown data set. Discussion: Our machine learning model could improve the clinical diagnosis and management of depression through the use of a wearable medical device. Clinical trial registration: [https://jrct.niph.go.jp/latest-detail/jRCT1031210478], identifier [jRCT1031210478].