Recent insights into aquatic viruses: Emerging and reemerging pathogens, molecular features, biological effects, and novel investigative approaches

Aquatic viruses are naturally present in the aquatic environment and the number of viruses is staggering.Various multicellular organisms in aquatic ecosystems may be infected,cross-species transmitted,manipulated,and killed by aquatic viruses,which can lead to cascading ecological effects.The viruses in unicellular aquatic organisms can alter interactions between host individuals,and are essential in effecting or maintaining the dynamics of aquatic microbial communities,horizontal gene transfer,biodiversity,and modulating ecological processes globally.Meanwhile,hosts also impact viral abundance and diversity.Microbial diversity drives multifunctionality in ecosystems,while viruses shape complex microbial communities and are crucial for ecosystem functioning.This review focuses on molecular,genetic,evolutionary,and ecosystemic advances related to emerging and reemerging aquatic viruses,presents the contexts,novel tools,and investigative approaches pertaining to the study of aquatic virology,and discusses the mechanisms by which viruses affect aquatic ecosystems.The paper provides an efficient and broadly-based blueprint for improving understanding of aquatic viruses.

Calcification-associated molecular traits and therapeutic strategies in hormone receptor-positive HER2-negative breast cancer

Objective:Mammographic calcifications are a common feature of breast cancer,but their molecular characteristics and treatment implications in hormone receptor-positive(HR+)/human epidermal growth factor receptor 2-negative(HER2−)breast cancer remain unclear.Methods:We retrospectively collected mammography records of an HR+/HER2−breast cancer cohort(n=316)with matched clinicopathological,genomic,transcriptomic,and metabolomic data.On the basis of mammographic images,we grouped tumors by calcification status into calcification-negative tumors,tumors with probably benign calcifications,tumors with calcification of lowmoderate suspicion for maligancy and tumors with calcification of high suspicion for maligancy.We then explored the molecular characteristics associated with each calcification status across multiple dimensions.Results:Among the different statuses,tumors with probably benign calcifications exhibited elevated hormone receptor immunohistochemical staining scores,estrogen receptor(ER)pathway activation,lipid metabolism,and sensitivity to endocrine therapy.Tumors with calcifications of high suspicion for malignancy had relatively larger tumor sizes,elevated lymph node metastasis incidence,Ki-67 staining scores,genomic instability,cell cycle pathway activation,and may benefit from cyclin-dependent kinase 4 and 6(CDK4/6)inhibitors.Conclusions:Our research established links between tumor calcifications and molecular features,thus proposing potential precision treatment strategies for HR+/HER2−breast cancer.

MOLECULAR DYNAMICS SIMULATION ON VIERATIONAL SPECTROSCOPIC FEATURES OF HYDROGEN BONDS IN CRYSTALLINE POLYMERS

Introduction The molecular dynamics simulation technique has recently proved to be a suitable alternative approachfor simulation of vibrational spectroscopy. In this study, molecular dynamics was utilized to understandlow frequency vibrations in highly ordered poly(ρ-phenylene terephthalmide) (PPTA). A key structuralfeature of this polymer is the presence of hydrogen bonds. There is little question that this strong localized

Pathological,molecular,and clinical characteristics of cholangiocarcinoma:A comprehensive review

Cholangiocarcinomas are a heterogeneous group of highly aggressive cancers that may arise anywhere within the biliary tree.There is a wide geographical variation with regards to its incidence,and risk-factor associations which may include liver fluke infection,primary sclerosing cholangitis,and hepatolithiasis amongst others.These tumours are classified into intrahepatic,perihilar and distal based on their anatomical location.Morphologically,intrahepatic cholangiocarcinomas are further sub-classified into small and large duct variants.Perihilar and distal cholangiocarcinomas are usually mucin-producing tubular adenocarcinomas.Cholangiocarcinomas develop through a multistep carcinogenesis and are preceded by dysplastic and in situ lesions.While clinical characteristics and management of these tumours have been extensively elucidated in literature,their ultra-structure and tumour biology remain relatively unknown.This review focuses on the current knowledge of pathological characteristics,molecular alterations of cholangiocarcinoma,and its precursor lesions(including biliary intraepithelial neoplasia,intraductal papillary neoplasms of the bile duct,intraductal tubulopapillary neoplasms and mucinous cystic neoplasm).

Extensive prediction of drug response in mutation-subtype-specific LUAD with machine learning approach

Lung cancer is the most prevalent cancer diagnosis and the leading cause of cancer death worldwide.Therapeutic failure in lung cancer(LUAD)is heavily influenced by drug resistance.This challenge stems from the diverse cell populations within the tumor,each having unique genetic,epigenetic,and phenotypic profiles.Such variations lead to varied therapeutic responses,thereby contributing to tumor relapse and disease progression.Methods:The Genomics of Drug Sensitivity in Cancer(GDSC)database was used in this investigation to obtain the mRNA expression dataset,genomic mutation profile,and drug sensitivity information of NSCLS.Machine Learning(ML)methods,including Random Forest(RF),Artificial Neurol Network(ANN),and Support Vector Machine(SVM),were used to predict the response status of each compound based on the mRNA and mutation characteristics determined using statistical methods.The most suitable method for each drug was proposed by comparing the prediction accuracy of different ML methods,and the selected mRNA and mutation characteristics were identified as molecular features for the drug-responsive cancer subtype.Finally,the prognostic influence of molecular features on the mutational subtype of LUAD in publicly available datasets.Results:Our analyses yielded 1,564 gene features and 45 mutational features for 46 drugs.Applying the ML approach to predict the drug response for each medication revealed an upstanding performance for SVM in predicting Afuresertib drug response(area under the curve[AUC]0.875)using CIT,GAS2L3,STAG3L3,ATP2B4-mut,and IL15RA-mut as molecular features.Furthermore,the ANN algorithm using 9 mRNA characteristics demonstrated the highest prediction performance(AUC 0.780)in Gefitinib with CCL23-mut.Conclusion:This work extensively investigated the mRNA and mutation signatures associated with drug response in LUAD using a machine-learning approach and proposed a priority algorithm to predict drug response for different drugs.

Pancreatic acinar cell carcinoma:A comprehensive review

Acinar cell carcinoma(ACC)is a rare pancreatic malignancy with distinctive clinical,molecular,and morphological features.The long-term survival of ACC patients is substantially superior to that of pancreatic adenocarcinoma patients.As there are no significant patient series about ACCs,our understanding of this illness is mainly based on case reports and limited patient series.Surgical resection is the treatment of choice for patients with the disease restricted to one organ;however,with recent breakthroughs in precision medicine,medicines targeting the one-of-a-kind molecular profile of ACC are on the horizon.There are no standard treatment protocols available for people in which a total surgical resection to cure the condition is not possible.As a result of shared genetic alterations,ACCs are chemosensitive to agents with activity against pancreatic adenocarcinomas and colorectal carcinomas.The role of neoadjuvant or adjuvant chemoradiotherapy has not been established.This article aims to do a comprehensive literature study and present the most recent information on acinar cell cancer.

Clinical meaning of BRAF mutation in Korean patients with advanced colorectal cancer

AIM: To evaluate the clinicopathological features of colorectal cancer (CRC) with a v-Raf murine sarcoma viral oncogene homolog B1 (BRAF) mutation and its molecular interaction with microsatellite instability (MSI) and v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) in patients with advanced CRCs.

AI-driven drug discovery from natural products

The latest review published in Nature Reviews Drug Discovery by Michael W.Mullowney and co-authors focuses on the use of artificial intelligence techniques,specifically machine learning,in natural product drug discovery.The authors discussed various applications of AI in this field,such as genome and metabolome mining,structural characterization of natural products,and predicting targets and biological activities of these compounds.They also highlighted the challenges associated with creating and managing large datasets for training algorithms,as well as strategies to address these obstacles.Additionally,the authors examine common pitfalls in algorithm training and offer suggestions for avoiding them.