First Report of Mango Stem-end Rot Caused by Pestalotiopsis kenyana in Yunnan Province, China.

Stem End Rot (SER) is a devastating post-harvest disease of mango fruits causing severe losses during storage. In 22 July 2023, 31 out of 50 intact mangoes (cv. Sensation) collected from five orchards in Huaping county (26°37'N 101°15') showed typical symptoms of SER after stored for 9 d in room temperature (24-28℃). Initially, small dark brown to black spots appeared around the fruit peduncle, which rapidly expanded through the pulp tissues. The symptomatic mangoes were surface disinfected by 3% NaClO for 30 s after soaking in 75% alcohol for 3 min, and cleaned by sterile water for 3 times. Tissues were cut from the edge of lesions, dried by sterile filter paper, transferred to PDA and cultured at 28 ℃ for 5 d (Tovar-Pedraza et al., 2020). The single-spore isolation method was used to obtain pure culture. Thirty eight isolates presented four distinct kind of morphology on PDA medium. Among them, 11 isolates with same morphology were significantly distinct from common pathogens of SER. The colonies were white and pale yellow on reverse side. Mycelia grew fast and reached the edge of 90 mm Petri dish after cultured for 5d. Pycnidia were black and scattered on the mycelial mats after 15-20 d. Conidia were fusoid, straight to slightly curved, four septa, and brown. Pigmented median cells doliiform, 14.97 - 18.62(16.11 ±0.89)×5.61- 7.28 (6.61±0.51) µm. Apical cell hyaline, subcylindrical; 1-3 tubular transparent apical appendages 12.27 - 16.68 (13.65±3.78)×1.14 - 1.99 (1.59±0.36) µm. Basal cell conical with a truncate base, hyaline, and 1-2 tubulose basal appendages with 2.85 - 7.97 (5.18±1.88)×0.99 - 1.85 (1.38±0.29) µm (n=50). These fungi were described as Pestalotiopsis kenyana. based on morphological characters (Maharachchikumbura et al., 2014) which were different from isolates characterized as other common SER pathogens (Botryosphaeria, Neofusicoccum). Based on morphology, HPSX-4 was selected for further identification. ITS region, tef1-α, ß-tub of HPSX-4 were amplified and sequenced (Xun et al., 2023). The sequences were deposited in GenBank (ITS:OR889126, tef1-α:OR913431, ß-tub: OR913432). The ITS, tef1-α, ß-tub sequence of HPSX-4 showed 100% (525/525)，99.59% (241/242), and 100% (742/742) identity to the P. kenyana CBS442.67 sequences (ITS: NR147549，tef1-α: KM199502, ß-tub: KM199395), respectively. HPSX-4 clustered with P. kenyana CBS 442.67 (type strain) based on maximum likelihood method by MEGA 7.0.21(Minh et al., 2013). Pathogenicity test was performed on 12 healthy mangoes (cv. Golek) by placing mycelial plugs around the peduncle and the middle of the fruit by pin-prick method according to Feng et al.(2023). Sterile PDA were used as control (three mangoes). Every inoculated fruit was incubated at 28°C, 95% ± 3% humidity with three replicates for each treatment. The experiment was repeated three times. Typical symptoms of SER were observed. There were no symptoms in the control group. The strain was reisolated and identified as P. kenyana with the method mentioned above which fulfilled Koch's postulates. This is the first report of P. kenyana causing SER disease on Mangifera indica L.. This study expands our understanding of the pathogen range of mango SER which conducive to prevent and control the SER caused by P. kenyana.

Enhancing the detonation performance of azobistriazole energetic derivatives via inducing N-oxide groups.

The N-oxide strategy plays a crucial role in regulating the performance and safety of energetic materials. This study mainly addresses the question of how the N-oxide group affects the properties of azobistriazole and its derivatives. Our findings indicate that the N-oxide group can increase the density of the system, and its effect on the enthalpy of formation depends on the specific situation. The N-oxide groups can effectively improve the density and energetic properties. Some of the energetic derivatives containing N-oxide groups have a density as high as 2.097 g cm-3 (D3-NO(2)) and a detonation velocity as high as 10 275 m s-1 (C6-NO(2)). The effect of N-oxide groups on the enthalpy of formation depends on the specific circumstances. The effect of N-oxide groups on the stability of azobistriazole energetic derivatives is relatively complex. Among them, the N-oxide group on the triazole ring has an opposite effect on the bond dissociation enthalpy of functional groups. When the N-oxide group is on the 1,2,3-triazole ring, it can improve C-R (R is equal to C(NO2)3, NF2, NHNO2, NO2, and ONO2 respectively) bond dissociation enthalpy, and when it is on the 1,2,4-triazole ring, it will reduce the C-R bond dissociation enthalpy. When the N-oxide group is located on the azo bond, the bond dissociation enthalpy of the azo bond will be significantly reduced. This article systematically explores the effect of N-oxide groups on the properties of azobistriazole energetic derivatives, which will help people better utilize N-oxide groups to design and synthesize new energetic materials.

NR1H4 disease: rapidly progressing neonatal intrahepatic cholestasis and early death.

BACKGROUND: Clinical studies on progressive familial intrahepatic cholestasis (PFIC) type 5 caused by mutations in NR1H4 are limited. METHODS: New patients with biallelic NR1H4 variants from our center and all patients from literature were retrospectively analyzed. RESULTS: Three new patients were identified to be carrying five new variants. Liver phenotypes of our patients manifests as low-γ-glutamyl transferase cholestasis, liver failure and related complications. One patient underwent liver transplantation (LT) and survived, and two other patients died without LT. Nine other patients were collected through literature review. Twelve out of 13 patients showed neonatal jaundice, with the median age of onset being 7 days after birth. Reported clinical manifestations included cholestasis (13/13, 100%), elevated AFP (11/11, 100%), coagulopathy (11/11, 100%), hypoglycemia (9/13, 69%), failure to thrive (8/13, 62%), splenomegaly (7/13, 54%), hyperammonemia (7/13, 54%), and hepatomegaly (6/13, 46%). Six of 13 patients received LT at a median age of 6.2 months, and only one patient died of acute infection at one year after LT. Other 7 patients had no LT and died with a median age of 5 months (range 1.2-8). There were 8 patients with homozygous genotype and 5 patients with compound heterozygous genotype. In total, 13 different variants were detected, and 5 out of 12 single or multiple nucleotides variants were located in exon 5. CONCLUSIONS: We identified three newly-diagnosed patients and five novel mutations. NR1H4-related PFIC typically cause progressive disease and early death. LT may be the only lifesaving therapy leading to cure.

Can large language models provide secondary reliable opinion on treatment options for dermatological diseases?

OBJECTIVE: To investigate the consistency and reliability of medication recommendations provided by ChatGPT for common dermatological conditions, highlighting the potential for ChatGPT to offer second opinions in patient treatment while also delineating possible limitations. MATERIALS AND METHODS: In this mixed-methods study, we used survey questions in April 2023 for drug recommendations generated by ChatGPT with data from secondary databases, that is, Taiwan's National Health Insurance Research Database and an US medical center database, and validated by dermatologists. The methodology included preprocessing queries, executing them multiple times, and evaluating ChatGPT responses against the databases and dermatologists. The ChatGPT-generated responses were analyzed statistically in a disease-drug matrix, considering disease-medication associations (Q-value) and expert evaluation. RESULTS: ChatGPT achieved a high 98.87% dermatologist approval rate for common dermatological medication recommendations. We evaluated its drug suggestions using the Q-value, showing that human expert validation agreement surpassed Q-value cutoff-based agreement. Varying cutoff values for disease-medication associations, a cutoff of 3 achieved 95.14% accurate prescriptions, 5 yielded 85.42%, and 10 resulted in 72.92%. While ChatGPT offered accurate drug advice, it occasionally included incorrect ATC codes, leading to issues like incorrect drug use and type, nonexistent codes, repeated errors, and incomplete medication codes. CONCLUSION: ChatGPT provides medication recommendations as a second opinion in dermatology treatment, but its reliability and comprehensiveness need refinement for greater accuracy. In the future, integrating a medical domain-specific knowledge base for training and ongoing optimization will enhance the precision of ChatGPT's results.

Biological characterization of emerging fungal pathogen Colletotrichum associated with mango (Mangifera indica L.) post-harvest anthracnose from Vietnam.

BACKGROUND: Post-harvest anthracnose (PHA) of mango is a devastating disease, which results in huge loss to mango producers and importers. Various species of PHA, diverse pathogenicity, and different resistance towards fungicides make it essential to evaluate the pathogen taxonomic status and biological characterization. METHODS AND RESULTS: Two strains DM-1 and DM-2 isolated from the fruit of DaQing mango from Vietnam were identified as Colletotrichum fructicola and C. asianum respectively, based on the morphological features, along with the phylogenetic tree of ITS and ApMat combined sequences. The growth status of different Colletotrichum strains under different conditions was analyzed to reveal the biological characteristics. The optimum growth temperature of DM-1 and DM-2 was 28 °C and mycelia grew rapidly in the dark. Both strains could grow in media with pH 4-11, while the optimum pH value was 6. Maltose and soluble starch were the most suitable carbon source for DM-1 and DM-2 respectively, and the peptone was the most suitable nitrogen source for both strains. The lethal temperatures were recorded as 55 °C 5 min for DM-1, and 50 °C 10 min for DM-2. CONCLUSIONS: To the best of our knowledge, it is the first study reporting the identification of the pathogens: C. fructicola and C. asianum responsible for postharvest fruit anthracnose of mango in Vietnam.

Regulation of lipid droplets and cholesterol metabolism in adrenal cortical cells.

The adrenal gland is composed of two distinctly different endocrine moieties. The interior medulla consists of neuroendocrine chromaffin cells that secrete catecholamines like adrenaline and noradrenaline, while the exterior cortex consists of steroidogenic cortical cells that produce steroid hormones, such as mineralocorticoids (aldosterone), glucocorticoids (cortisone and cortisol) and androgens. Synthesis of steroid hormones in cortical cells requires substantial amounts of cholesterol, which is the common precursor for steroidogenesis. Cortical cells may acquire cholesterol from de novo synthesis and uptake from circulating low- and high-density lipoprotein particles (LDL and HDL). As cholesterol is part of the plasma membrane in all mammalian cells and an important regulator of membrane fluidity, cellular levels of free cholesterol are tightly regulated. To ensure a robust supply of cholesterol for steroidogenesis and to avoid cholesterol toxicity, cortical cells store large amounts of cholesterol as cholesteryl esters in intracellular lipid droplets. Cortical steroidogenesis relies on both mobilization of cholesterol from lipid droplets and constant uptake of circulating cholesterol to replenish lipid droplet stores. This chapter will describe mechanisms involved in cholesterol uptake, cholesteryl ester synthesis, lipid droplet formation, hydrolysis of stored cholesteryl esters, as well as their impact on steroidogenesis. Additionally, animal models and human diseases characterized by altered cortical cholesteryl ester storage, with or without abnormal steroidogenesis, will be discussed.

Adjustable Cuffless Smartphone Attachment (ACSA+) for Estimation of Blood Pressure Trends: A Pilot Study.

Among the elderly, hypertension remains one of the prevalent health conditions, which requires monitoring and intervention strategies. Nevertheless, regular reporting of blood pressure (BP) from these individuals still poses multiple challenges. However, most people own cell phone and are engaged in phone conversations daily. Here, we propose an adjustable cuffless smartphone attachment (ACSA+) equipped with a PPG sensor for the estimation of BP during phone conversations. ACSA+ can be easily attached to the back of any modern cell phone. ACSA+ will help to continuously collect BP data and store it as a trend line.

Artificial Intelligence Approach for Severe Dengue Early Warning System.

Dengue fever is a viral infectious disease transmitted through mosquito bites, and has symptoms ranging from mild flu-like symptoms to deadly complications. Dengue fever is one of the global burden diseases which annually have 50-100 million cases with 500,000 cases of severe dengue fever, of which 22,000 deaths occur mostly in children. Despite the discovery of vaccines, vector control is still the main approach for prevention efforts. Early detection and accessibility to medical care can reduce severe Dengue mortality rate from 50% to 2%. In the previous study, both statistical and machine learning methods have the potential for predicting a Dengue outbreak, but the study is still fragmented and limited on implementing the generated model into an early warning system application. In this study, we developed an artificial intelligence model with spatiotemporal to predict Dengue outbreak and Dengue incidence case which is ready to be implemented into an early warning system application. Indonesia, especially Semarang City, has experienced an endemic Dengue. We used Semarang City spatiotemporal, meteorological, climatological, and Dengue surveillance epidemiology data from January 2014 to December 2021 in 16 districts of Semarang City. We reviewed 7208 samples from 16 districts and 1 city per week during 8 years. The entire dataset was divided into training (80%) and testing (20%) to develop a prediction model. We used machine learning and Long Short Term Memory (LSTM) to predict Dengue outbreak 1 week before the event for each district. and machine learning to predict Dengue incident cases 1 week before the event for each district. Accuracy, area under the receiver operating characteristic curve (AUROC), precision, recall, and F1 score were considered to evaluate the Dengue outbreak prediction model. The Dengue incidence cases prediction model will evaluate using Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-Squared (R2). Extra Trees Classifier model shown outperform in Dengue outbreak prediction, with accuracy 0.8925, AUROC 0. 9529, Recall 0.6117, precision 0.8880, and F1 score 0.7238. CatBoost Regressor model is shown to outperform in Dengue incidence cases prediction, with R2 0.5621, MAE 0.6304, MSE 1.1997, and RMSE 1.0891. The study proves that Artificial Intelligence (AI) with a spatiotemporal approach can give higher performance in Dengue outbreak and incidence cases prediction. Utilization of AI approaches that are sensitive with spatiotemporal feasibility to implement in Dengue early warning system application may contribute to increase the policy makers and community attention to do accurate community-based vector control.

Machine-Learning Based Risk Assessment for Cancer Therapy-Related Cardiac Adverse Events Among Breast Cancer Patients.

The study aims to develop machine-learning models to predict cardiac adverse events in female breast cancer patients who receive adjuvant therapy. We selected breast cancer patients from a retrospective dataset of the Taipei Medical University Clinical Research Database and Taiwan Cancer Registry between January 2004 and December 2020. Patients were monitored at the date of prescribed chemo- and/or -target therapies until cardiac adverse events occurred during a year. Variables were used, including demographics, comorbidities, medications, and lab values. Logistics regression (LR) and artificial neural network (ANN) were used. The performance of the algorithms was measured by the area under the receiver operating characteristic curve (AUC). In total, 1321 patients (an equal 15039 visits) were included. The best performance of the artificial neural network (ANN) model was achieved with the AUC, precision, recall, and F1-score of 0.89, 0.14, 0.82, and 0.2, respectively. The most important features were a pre-existing cardiac disease, tumor size, estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2), cancer stage, and age at index date. Further research is necessary to determine the feasibility of applying the algorithm in the clinical setting and explore whether this tool could improve care and outcomes.

Speech Emotion Recognition Applied to Real-World Medical Consultation.

Since 2020, the COVID-19 epidemic has changed our lives in healthcare behaviors. Forced to wear masks influenced doctor-patient interaction perceptions truly, thus, to build a satisfying relationship is not just empathize with facial expressions. The voice becomes more important for the sake of conquering the burden of masks. Hence, verbal and non-verbal communication will be crucial criteria for doctor-patient interaction during medical consultations and other conversations. In these years, speech emotion recognition has been a popular research domain. In spite of abundant work conducted, nonverbal emotion recognition in medical scenarios is still required to reveal. In this study, we investigate YAMNet transfer learning on Chinese Mandarin speech corpus NTHU-NTUA Chinese Interactive Emotion Corpus (NNIME) and use real-world dermatology clinic recording to test the generalization capability. The results showed that the accuracy validated on NNIME data was 0.59 for activation prediction and 0.57 for valence. Furthermore, the validation accuracy on the doctor-patient dataset was 0.24 for activation and 0.58 for valence, respectively.

Facial Mimicry and Doctor-Patient Satisfaction: The Feasibility of Artificial Empathy in a Clinical Video Data.

Good nonverbal communication between doctor and patient is essential for achieving a successful and therapeutic doctor-patient relationship. Increasing evidence has shown that nonverbal communication mimicry, particularly facial mimicry, where one mirrors another's facial expressions, is linked to empathy and emotion recognition. Empathy is also the key driver of patient satisfaction. This study explores how facial expressions and facial mimicry influence doctor-patient satisfaction during a clinical encounter. We used a facial emotion recognition-based artificial empathy model to analyze 315 recorded clinical video data of doctors and patients in a dermatology outpatient clinic. The results show a significant negative correlation between patients' emotions of sadness and neutral and doctor satisfaction, but no correlation between the duration of doctors mimicking patient emotions and patient satisfaction. These findings provide valuable insights into the future design of systems that can further enhance clinician awareness to maintain communication skills in the search for better doctor-patient satisfaction.

Molecular characterization of a novel mycovirus from binucleate Rhizoctonia AG-A strain A46.

The complete genome sequence of a positive-sense single-stranded RNA (+ ssRNA) virus, Rhizoctonia beny-like virus 1 (RBLV1), isolated from binucleate Rhizoctonia AG-A strain A46, was determined. The RBLV1 genome is 10,280 nt in length and contains a short stretch of adenines at the 3' terminus. It contains a single open reading frame (ORF) encoding a 376.30-kDa protein with viral helicase and RNA-dependent RNA polymerase (RdRp) motifs. The encoded protein exhibited the highest sequence similarity to Rhizoctonia cerealis beny-like virus 0928-1 (RcBeLV 0928-1, 45.25%), with a sequence coverage of 63%. Phylogenetic analysis based on ORF protein sequences revealed that RBLV1 is a novel unclassified mycovirus.

Relationship of Glucagon-like Peptide 1 and Peptide YY with Catch-up Growth in Children Born Small for Gestational Age

Objective: Children born small for gestational age (SGA) are at a greater risk of developing insulin resistance, type 2 diabetes, and cardiovascular disease in adulthood. Gastrointestinal peptides, some secreted by intestinal L cells, regulate glucose and lipid metabolism and act on the hypothalamus to regulate energy homeostasis. The aim of this study was to explore whether gastrointestinal peptides are involved in metabolic disorders in SGA, which remains unclear. Methods: The secretion of glucagon-like peptide 1 (GLP-1) and peptide YY (PYY) were investigated in prepubertal children born SGA, the differences between catch-up growth and persistent short stature were compared, and correlation with glucose and lipid metabolism was analyzed. GLP-1, PYY, insulin-like growth factor 1, glucose, insulin, and lipid concentrations were analyzed in prepubertal children aged 4-10 years, stratified into three groups: short-SGA (SGA-s), catch-up growth SGA, and normal growth appropriate for gestational age (AGA). Results: Fasting GLP-1 and PYY concentrations were significantly lower in the SGA group than in the AGA group (p<0.05), and the GLP-1 level in infants born SGA with catch-up growth was lower than that in the SGA-s group (p<0.05). In the SGA population, GLP-1 showed a weak negative correlation with catch-up growth (r=-0.326) and positive correlation with fasting insulin (r=0.331). Conclusion: Lower GLP-1 concentrations may be associated with abnormal glucose metabolism in prepubertal children born SGA with catch-up growth. This is indirect evidence that impaired intestinal L cell function may be involved in the development of metabolic complications in SGA children.

Study on color, aroma, and taste formation mechanism of large-leaf yellow tea during an innovative manufacturing process.

This study used samples processed with an innovative manufacturing process to explore the dynamic changes of large-leaf yellow tea (LYT) in color, aroma, and taste substances, and the quality components were most significantly affected in the stages of first pile-yellowing (FP) and over-fired drying (TD). In this process, the moisture and temperature conditions caused chlorophyll degradation, Maillard reactions, caramelization reactions, and isomerization of phenolic substances, forming the quality of LYT. Specifically, chlorophyll degradation favored the formation of color quality; the taste quality was determined by the content of soluble sugars, amino acids, catechins, etc.; the aroma quality was dependent on the content changes of alcohols and aldehydes, as well as the increase of sweet and roasting aroma substances in the third drying stage. Additionally, twelve key aroma components, including linalool, (E)-ß-ionone, 2,3-diethyl-5-methyl-pyrazine, etc., were identified as contributors to revealing LYT rice crust-like and sweet aroma formation mechanism.

Call for the responsible artificial intelligence in the healthcare.

The integration of artificial intelligence (AI) into healthcare is progressively becoming pivotal, especially with its potential to enhance patient care and operational workflows. This paper navigates through the complexities and potentials of AI in healthcare, emphasising the necessity of explainability, trustworthiness, usability, transparency and fairness in developing and implementing AI models. It underscores the 'black box' challenge, highlighting the gap between algorithmic outputs and human interpretability, and articulates the pivotal role of explainable AI in enhancing the transparency and accountability of AI applications in healthcare. The discourse extends to ethical considerations, exploring the potential biases and ethical dilemmas that may arise in AI application, with a keen focus on ensuring equitable and ethical AI use across diverse global regions. Furthermore, the paper explores the concept of responsible AI in healthcare, advocating for a balanced approach that leverages AI's capabilities for enhanced healthcare delivery and ensures ethical, transparent and accountable use of technology, particularly in clinical decision-making and patient care.

Crowdsource authoring as a tool for enhancing the quality of competency assessments in healthcare professions.

The current Objective Structured Clinical Examination (OSCE) is complex, costly, and difficult to provide high-quality assessments. This pilot study employed a focus group and debugging stage to test the Crowdsource Authoring Assessment Tool (CAAT) for the creation and sharing of assessment tools used in editing and customizing, to match specific users' needs, and to provide higher-quality checklists. Competency assessment international experts (n = 50) were asked to 1) participate in and experience the CAAT system when editing their own checklist, 2) edit a urinary catheterization checklist using CAAT, and 3) complete a Technology Acceptance Model (TAM) questionnaire consisting of 14 items to evaluate its four domains. The study occurred between October 2018 and May 2019. The median time for developing a new checklist using the CAAT was 65.76 minutes whereas the traditional method required 167.90 minutes. The CAAT system enabled quicker checklist creation and editing regardless of the experience and native language of participants. Participants also expressed the CAAT enhanced checklist development with 96% of them willing to recommend this tool to others. The use of a crowdsource authoring tool as revealed by this study has efficiently reduced the time to almost a third it would take when using the traditional method. In addition, it allows collaborations to partake on a simple platform which also promotes contributions in checklist creation, editing, and rating.

Fatty acids abrogate the growth-suppressive effects induced by inhibition of cholesterol flux in pancreatic cancer cells.

BACKGROUND: Despite therapeutic advances, the prognosis of pancreatic ductal adenocarcinoma (PDAC) remains extremely poor. Metabolic reprogramming is increasingly recognized as a key contributor to tumor progression and therapy resistance in PDAC. One of the main metabolic changes essential for tumor growth is altered cholesterol flux. Targeting cholesterol flux appears an attractive therapeutic approach, however, the complex regulation of cholesterol balance in PDAC cells remains poorly understood. METHODS: The lipid content in human pancreatic duct epithelial (HPDE) cells and human PDAC cell lines (BxPC-3, MIA PaCa-2, and PANC-1) was determined. Cells exposed to eight different inhibitors targeting different regulators of lipid flux, in the presence or absence of oleic acid (OA) stimulation were assessed for changes in viability, proliferation, migration, and invasion. Intracellular content and distribution of cholesterol was assessed. Lastly, proteome profiling of PANC-1 exposed to the sterol O-acyltransferase 1 (SOAT1) inhibitor avasimibe, in presence or absence of OA, was performed. RESULTS: PDAC cells contain more free cholesterol but less cholesteryl esters and lipid droplets than HPDE cells. Exposure to different lipid flux inhibitors increased cell death and suppressed proliferation, with different efficiency in the tested PDAC cell lines. Avasimibe had the strongest ability to suppress proliferation across the three PDAC cell lines. All inhibitors showing cell suppressive effect disturbed intracellular cholesterol flux and increased cholesterol aggregation. OA improved overall cholesterol balance, reduced free cholesterol aggregation, and reversed cell death induced by the inhibitors. Treatment with avasimibe changed the cellular proteome substantially, mainly for proteins related to biosynthesis and metabolism of lipids and fatty acids, apoptosis, and cell adhesion. Most of these changes were restored by OA. CONCLUSIONS: The study reveals that disturbing the cholesterol flux by inhibiting the actions of its key regulators can yield growth suppressive effects on PDAC cells. The presence of fatty acids restores intracellular cholesterol balance and abrogates the alternations induced by cholesterol flux inhibitors. Taken together, targeting cholesterol flux might be an attractive strategy to develop new therapeutics against PDAC. However, the impact of fatty acids in the tumor microenvironment must be taken into consideration.