Introduction: Rhubarb is a frequently used and beneficial traditional Chinese medicine. Wild resources of these plants are constantly being depleted, meaning that rhubarb products have been subjected to an unparalleled level of adulteration. Consequentially, reliable technology is urgently required to verify the authenticity of rhubarb raw materials and commercial botanical drugs. Methods: In this study, the barcode-DNA high-resolution melting (Bar-HRM) method was applied to characterize 63 rhubarb samples (five Polygonaceae species: Rheum tanguticum, Rh. palmatum, Rh. officinale, Rumex japonicus and Ru. sp.) and distinguish the rhubarb contents of 24 traditional Chinese patent medicine (TCPM) samples. Three markers, namely ITS2, rbcL and psbA-trnH, were tested to assess the candidate DNA barcodes for their effectiveness in distinguishing rhubarb from its adulterants. A segment from ITS2 was selected as the most suitable mini-barcode to identify the botanical drug rhubarb in TCPMs. Then, rhubarbs and TCPM samples were subjected to HRM analysis based on the ITS2 barcode. Results: Among the tested barcoding loci, ITS2 displayed abundant sites of variation and was effective in identifying Polygonaceae species and their botanical origins. HRM analysis based on the ITS2 mini-barcode region successfully distinguished the authenticity of five Polygonaceae species and eight batches of TCPMs. Of the 18 TCPM samples, 66.7 % (12 samples) were identified as containing Rh. tanguticum or Rh. officinale. However, 33.3 % were shown to consist of adulterants. Conclusions: These results demonstrated that DNA barcoding combined with HRM is a specific, suitable and powerful approach for identifying rhubarb species and TCPMs, which is crucial to guaranteeing the security of medicinal plants being traded internationally.

UNASSIGNED: The PI3K protein is involved in the PI3K/AKT/mTOR pathway. Deregulation of this pathway through PIK3CA mutation is common in various tumors. The aim of this work is to identify hotspot mutation at exons 9 and 20 in Tunisian patients with sporadic or hereditary breast cancer.
UNASSIGNED: Hotspot mutations in exon 9 and exon 20 of the PIK3CA gene were identified by QPCR-High Resolution Melting followed by COLD-PCR and sequencing in 63 (42 sporadic cases and 21 hereditary cases) tumor tissues collected from Tunisian patient with breast cancer. MCF7, and BT20 breast cancer cell lines harboring the PIK3CA hotspot mutations E545K and H1047R in exon 9 and exon 20 respectively, were used as controls in HRM experiments.
UNASSIGNED: PIK3CA hotspot mutations were detected in 66.7% (28 out of 42) of sporadic BC cases, and in 14.3% (3 out of 21) of hereditary BC. The E545K and the H1048Y were the most prevalent mutations identified in patients with sporadic and hereditary BC, whereas the H1047R hotspot mutation was not found in our patients. Statistical analysis showed that PIK3CA mutation associated with an aggressive behavior in patients with sporadic BC, while it\'s correlated with age, tumor stage and tumor size in the group patients with hereditary breast cancer.
UNASSIGNED: Our results showed a novel PIK3CA hotspot mutation in Tunisian breast cancer patients detected by HRM-COLD-PCR. Moreover, the absence of PIK3CA hotspot mutation associated with good prognosis.

Although guidelines exist for identifying mixtures, these measures often occur at the end-point of analysis and are protracted. To facilitate early mixture detection, we integrated a high-resolution melt (HRM) mixture screening assay into the qPCR step of the forensic workflow, producing the integrated QuantifilerTM Trio-HRM assay. The assay, when coupled with a prediction tool, allowed for 75.0% accurate identification of the contributor status of a sample (single source vs. mixture). To elucidate the limitations of the developed qPCR-HRM assay, developmental validation studies were conducted assessing the reproducibility and samples with varying DNA ratios, contributors, and quality. From this work, it was determined that the integrated QuantifilerTM Trio-HRM assay is capable of accurately identifying mixtures with up to five contributors and mixtures at ratios up to 1:100. Further, the optimal performance concentration range was found to be between 0.025 and 0.5 ng/µL. With these results, evidentiary-like DNA samples were then analyzed, resulting in 100.0% of the mixture samples being accurately identified; furthermore, every time a sample was predicted as a single source, it was true, giving confidence to any single-source calls. Overall, the integrated QuantifilerTM Trio-HRM assay has exhibited an enhanced ability to discern mixture samples from single-source samples at the qPCR stage under commonly observed conditions regardless of the contributor\'s sex.

Invasive mold infections (IMIs) are associated with high morbidity, particularly in immunocompromised patients, with mortality rates between 40% and 80%. Early initiation of appropriate antifungal therapy can substantially improve outcomes, yet early diagnosis remains difficult to establish and often requires multidisciplinary teams evaluating clinical and radiological findings plus supportive mycological findings. Universal digital high-resolution melting (U-dHRM) analysis may enable rapid and robust diagnoses of IMI. A universal fungal assay was developed for U-dHRM and used to generate a database of melt curve signatures for 19 clinically relevant fungal pathogens. A machine learning algorithm (ML) was trained to automatically classify these pathogen curves and detect novel melt curves. Performance was assessed on 73 clinical bronchoalveolar lavage samples from patients suspected of IMI. Novel curves were identified by micropipetting U-dHRM reactions and Sanger sequencing amplicons. U-dHRM achieved 97% overall fungal organism identification accuracy and a turnaround time of ~4 hrs. U-dHRM detected pathogenic molds (Aspergillus, Mucorales, Lomentospora, and Fusarium) in 73% of 30 samples classified as IMI, including mixed infections. Specificity was optimized by requiring the number of pathogenic mold curves detected in a sample to be >8 and a sample volume to be 1 mL, which resulted in 100% specificity in 21 at-risk patients without IMI. U-dHRM showed promise as a separate or combination diagnostic approach to standard mycological tests. U-dHRM\'s speed, ability to simultaneously identify and quantify clinically relevant mold pathogens in polymicrobial samples, and detect emerging opportunistic pathogens may aid treatment decisions, improving patient outcomes.
OBJECTIVE: Improvements in diagnostics for invasive mold infections are urgently needed. This work presents a new molecular detection approach that addresses technical and workflow challenges to provide fast pathogen detection, identification, and quantification that could inform treatment to improve patient outcomes.

暂无摘要。

OBJECTIVE: Strongyloides stercoralis is a parasite with special characteristics presenting it as a unique nematode. Iran is an endemic area for S. stercoralis. In this study, nested-qPCR-high resolution melting (HRM) technology was applied on some human isolates of S. stercoralis from this country by focusing on evolutionary genetics analysis.
METHODS: Twelve human isolates of S. stercoralis were collected from four endemic provinces of Iran. Genomic DNA was extracted from a single filariform larva for every isolate. Using specific primers targeting partial regions in cox1 gene, nested-qPCR-HRM was performed and melting-curve profiles were analyzed alongside the evaluation of genetic proximity and phylogenetic analysis using MEGA7 and DnaSP5 software.
RESULTS: The melting temperature (Tm) values of the isolates were 77.9 °C-78.3 °C. All isolates from Guilan, Mazandaran, and Khouzestan Provinces shared Tm values of 78.2 °C to 78.3 °C, while the isolates from Hormozgan Province showed Tm values of 77.9 °C, 78.0 °C, and 78.1 °C. The phylogenetic tree illustrated that the sequences of the current study included nine haplotypes. Tajima\'s D index analyses showed that cox1 gene in S. stercoralis isolates was negative (Tajima\'s D = - 0.27).
CONCLUSIONS: The isolates were divided into five temperature groups. Although HRM assay compared to PCR sequencing identified more limited genetic changes, it revealed that the mean of Tm of the isolates from Hormozgan Province was lower than those of other provinces and represented specific haplotypes for this geographical region on the phylogenetic tree.

The functions of human resource management (HRM) have changed radically in the past 20 years due to market and technological forces, becoming more cross-functional and data-driven. In the age of AI, the role of HRM professionals in organizations continues to evolve. Artificial intelligence (AI) is transforming many HRM functions and practices throughout organizations creating system and process efficiencies, performing advanced data analysis, and contributing to the value creation process of the organization. A growing body of evidence highlights the benefits AI brings to the field of HRM. Despite the increased interest in AI-HRM scholarship, focus on human-AI interaction at work and AI-based technologies for HRM is limited and fragmented. Moreover, the lack of human considerations in HRM tech design and deployment can hamper AI digital transformation efforts. This paper provides a contemporary and forward-looking perspective to the strategic and human-centric role HRM plays within organizations as AI becomes more integrated in the workplace. Spanning three distinct phases of AI-HRM integration (technocratic, integrated, and fully-embedded), it examines the technical, human, and ethical challenges at each phase and provides suggestions on how to overcome them using a human-centric approach. Our paper highlights the importance of the evolving role of HRM in the AI-driven organization and provides a roadmap on how to bring humans and machines closer together in the workplace.

The goal of this work was to use the Organizational Culture Assessment Instrument (OCAI) diagnostic method to find out the existing and preferred organizational culture in companies in the Czech Republic and then to evaluate their dependence on the size of the organizations. Data were collected in 2019-2021 and evaluated using Microsoft Excel and IBM SPSS 24 statistical program, e.g., descriptive statistics tools, one-sample z-test, analysis of variance and post hoc test (Tukey\'s honest significant difference - HSD). The research was conducted on a sample of 962 companies across the entire republic and fields. The results of the study show that clan culture prevails in the Czech Republic, which was dominant in all six dimensions. Enterprises in the Czech Republic have a mix of organizational cultures in the following order: (1) clan culture (31.72%), (2) hierarchical culture (25.46%), (3) market culture (21.5%), and (4) adhocratic culture (21.28%). However, with regard to the desired cultural mix, this order changes as follows: (1) clan (35.3%), (2) hierarchical (22.91%), (3) adhocratic (22.63%), and (4) market culture (19.17%). Furthermore, it was found that a statistically significant difference was observed in the assessment of organizational culture depending on the size of the organization in the Czech Republic. A limitation of the research could be the unequal number of respondents in 2020 and 2021, which does not allow comparing differences in the time period. This work can serve as a comparative basis of organizational culture with another national culture.

Human resource management (HRM) in healthcare is an important component in relation to the quality and efficiency of healthcare delivery. However, a comprehensive overview is lacking to assess and track the current status and trends of HRM research in healthcare. This study aims to describe the current situation and global trends in HRM research in healthcare as well as to indicate the frontiers and future directions of research. The research methodology is based on bibliometric mapping using scientific visualization software (VOSviewer). The data were collected from the Web of Science(WoS) core citation database. After applying the search criteria, we retrieved 833 publications, which have steadily increased over the last 30 years. In addition, 93 countries and regions have published relevant research. The United States and Australia have made significant contributions in this area. Current research articles focus on topics clustered into performance, hospital/COVID-19, job satisfaction, human resource management, occupational/mental health, and quality of care. The most frequently co-occurring keywords are human resource management, job satisfaction, nurses, hospitals, health services, quality of care, COVID-19, and nursing. There is limited research on compensation management and employee relations management, so the current HRM research field still has not been able to present a complete and systematic roadmap. We propose that our colleagues should consider focusing on these research gaps in the future.

UNASSIGNED: Invasive mold infections (IMIs) such as aspergillosis, mucormycosis, fusariosis, and lomentosporiosis are associated with high morbidity and mortality, particularly in immunocompromised patients, with mortality rates as high as 40% to 80%. Outcomes could be substantially improved with early initiation of appropriate antifungal therapy, yet early diagnosis remains difficult to establish and often requires multidisciplinary teams evaluating clinical and radiological findings plus supportive mycological findings. Universal digital high resolution melting analysis (U-dHRM) may enable rapid and robust diagnosis of IMI. This technology aims to accomplish timely pathogen detection at the single genome level by conducting broad-based amplification of microbial barcoding genes in a digital polymerase chain reaction (dPCR) format, followed by high-resolution melting of the DNA amplicons in each digital reaction to generate organism-specific melt curve signatures that are identified by machine learning.
UNASSIGNED: A universal fungal assay was developed for U-dHRM and used to generate a database of melt curve signatures for 19 clinically relevant fungal pathogens. A machine learning algorithm (ML) was trained to automatically classify these 19 fungal melt curves and detect novel melt curves. Performance was assessed on 73 clinical bronchoalveolar lavage (BAL) samples from patients suspected of IMI. Novel curves were identified by micropipetting U-dHRM reactions and Sanger sequencing amplicons.
UNASSIGNED: U-dHRM achieved an average of 97% fungal organism identification accuracy and a turn-around-time of 4hrs. Pathogenic molds (Aspergillus, Mucorales, Lomentospora and Fusarium) were detected by U-dHRM in 73% of BALF samples suspected of IMI. Mixtures of pathogenic molds were detected in 19%. U-dHRM demonstrated good sensitivity for IMI, as defined by current diagnostic criteria, when clinical findings were also considered.
UNASSIGNED: U-dHRM showed promising performance as a separate or combination diagnostic approach to standard mycological tests. The speed of U-dHRM and its ability to simultaneously identify and quantify clinically relevant mold pathogens in polymicrobial samples as well as detect emerging opportunistic pathogens may provide information that could aid in treatment decisions and improve patient outcomes.