Clinical Medicine Research Articles

Self-Internal Standard Fluorescence for Ultrasensitive Detecting of mtDNA to Evaluate Matrilineal Genetic Defect Levels.

Mitochondrial DNA (mtDNA) is a unique genetic material characterized by maternal inheritance. It possesses a circular structure devoid of histone protection and exhibits low cellular abundance, which poses great challenges for its sensitive and selective detection at the living cell level. Herein, we have designed three bis-naphthylimide probes with varying linker lengths (NANn-OH, n = 0, 2, 6), facilitating the formation of distinct twisted or folded molecular conformations in the free state. These probes emit the red fluorescence around 627 nm with different fluorescence quantum yields (ΦNAN0-OH = 0.0016, ΦNAN2-OH = 0.0136, and ΦNAN6-OH = 0.0125). When encountering mtDNA (0.4-3.4 μg/mL), these probes undergo conformational changes depending on the length of the attached C-strand and exhibit a gradually increasing fluorescence signal around 453 nm. The fluorescence intensity increased to 13.5-fold, 1.9-fold, and 8.2-fold, respectively. Notably, the red fluorescence intensities around 627 nm remain constant throughout this process, thus serving as an inherent correction mechanism for proportional fluorescence signal enhancement to improve selectivity and sensitivity. NAN0-OH, NAN2-OH, and NAN6-OH showed good linearity for mtDNA in the range of 0.4-3.4 μg/mL with detection limits of LODNAN0-OH = 1.04 μg/mL, LODNAN2-OH = 1.10 μg/mL, and LODNAN6-OH = 1.15 μg/mL. Cellular experiments reveal that NAN6-OH effectively monitors curcumin-induced mtDNA damage in HepG-2 cells while enabling monitoring of genetic mtDNA damage. We anticipate that this tool holds significant potential for the precise evaluation of maternal genetic defects, thereby enhancing hypersensitive assessment in clinical medicine.

"Even Though the System Had Failed Him His Entire Life, We Were Failing Him Yet Again": How Clinical, Welfare, and Penal Medicine Interact to Drive Health Inequities and Medical Moral Injury.

Adam, a justice-involved young man, was brought into the emergency department at the county hospital in cardiogenic shock due to a recurring episode of injection-drug-use-related infective endocarditis (IDU-IE). Adam had initiated injection opioid use in prison. He was surgically treated for the previous episodes of IDU-IE but was unable to fully recover due to limitations in care within penal medicine. This case report explores the prison as a determinant of health, interactions between clinical, welfare, and penal medicine, to produce and maintain health inequities, and structural drivers of physician moral injury through an interview with Adam and reflexive writings from emergency medicine physicians. This case demonstrates the need for three types of structural health interventions: (1) restorative justice, community-based reentry programs, and housing as welfare medicine, (2) increased harm reduction services across healthcare, especially penal medicine, and (3) equitable institutional protocols (contrary to ambiguous guidelines) to treat clinical conditions like IDU-IE that disproportionately impact structurally vulnerable patients.

One Case of Solar Dermatitis Treated with Umbilical Needle "Four Positions" Combined with Body Acupuncture

Solar dermatitis is a kind of hypersensitive skin disease, the main clinical manifestations are redness, swelling, itching and burning. A case of solar dermatitis treated with umbilical acupuncture "four positive positions" combined with body acupuncture by Professor Chen Chaoming is introduced, which provides a new way of thinking for the treatment of solar dermatitis by clinical Chinese medicine external therapy.

Metformin beyond type 2 diabetes: Emerging and potential new indications.

Metformin is best known as a foundational therapy for type 2 diabetes but is also used in other contexts in clinical medicine with a number of emerging and potential indications. Many of its beneficial effects may be mediated by modest effects on weight loss and insulin sensitivity, but it has multiple other known mechanisms of action. Current clinical uses beyond type 2 diabetes include: polycystic ovarian syndrome; diabetes in pregnancy/gestational diabetes; prevention of type 2 diabetes in prediabetes; and adjunct therapy in type 1 diabetes. As metformin has been in clinical use for almost 70 years, much of the underpinning evidence for its use in these conditions is, by definition, based on trials conducted before the advent of contemporary evidence-based medicine. As a result, some of the above-established uses are 'off-label' in many regulatory territories and their use varies accordingly in different countries. Going forward, several current 'repurposing' investigational uses of metformin are also being investigated: prevention of cancer (including in Li Fraumeni syndrome), renal protection, Alzheimer's disease, metabolic dysfunction-associated steatotic liver disease and promotion of healthy ageing. Despite the longevity of metformin and its important current roles beyond type 2 diabetes in clinical medicine, it has further potential and much research is ongoing.

The application of blended teaching in medical practical course of clinical skills training

BackgroundBlended teaching is an effective approach that combines online and offline teaching methods, leading to improved outcomes in medical education compared to traditional offline teaching. In this study, we examined the impact of blended teaching in clinical skills training, a medical practice course.MethodsThis study involved forty-eight undergraduate students studying clinical medicine in the fifth semester at Wuhan University of Science and Technology. The students were divided into two groups: the control group, which received traditional offline teaching, and the experimental group, which received hybrid teaching. Following the completion of the 4-month course, both groups underwent the Objective Structured Clinical Examination (OSCE) to evaluate their proficiency in clinical skills. Furthermore, the experimental group was given a separate questionnaire to gauge their feedback on the Blended Teaching approach.ResultsBased on the OSCE scores, the experimental group outperformed the control group significantly (P<0.05). The questionnaire results indicated that a majority of students (54.2%, 3.71 ± 1.06) believed that blended teaching is superior to traditional offline teaching, and a significant number of students (58.3%, 3.79 ± 1.15) expressed their willingness to adopt blended teaching in other courses. Furthermore, students in the experimental group displayed varying levels of interest in different teaching contents, with emergency medicine (79.2%), internal medicine (70.8%), and surgery (66.7%) being the most popular among them.ConclusionsThis research demonstrates for the first time that blended teaching can achieve a good pedagogical effectiveness in the medical practice course, clinical skills training and practice. Moreover, in different teaching contents, the teaching effects are different. In the content of Emergency Medicine and Surgery, which is more attractive to students, the application of blended teaching could result in a better pedagogical outcome than other contents.

Synergistic potential of gellan gum methacrylate and keratin hydrogel for visceral hemostasis and skin tissue regeneration

In recent years, the development of biodegradable hydrogels as an alternative over the traditional wound dressing has become increasingly significant. These specific hydrogels are able to offer suitable microenvironments to further aid the process of tissue or organ regeneration. However, application of biodegradable hydrogels in clinical medicine remains uncommon due to most biodegradable hydrogels struggle with achieving satisfactory adhesiveness property, high mechanical support and cell compatibility simultaneously. In order to overcome these constraints and enhance the applicability of biodegradable hydrogels, methods have been employed in this study.By reacting gellan gum with methacrylic anhydride and incorporating a biodegradable protein, keratin, we endowed the hydrogels with high pliability via photo-polymerization chain extension, thereby obtaining a biodegradable hydrogel with exceptional properties. Through a series of in vitro tests, GGMA/keratin hydrogels exhibited great cell compatibility via providing an appropriate environment for cell proliferation. Furthermore, this hydrogel not only exhibits extraordinary adhesive ability on visceral tissues but also extends to scenarios involving skin or organ damage, offering valuable assistance in wound healing. Our design provides a suitable platform for cell proliferation and tissue regeneration, which shows prospects for future medical research and clinical applications.

Degradation Behavior of Medical MgZZC-1 in Various Simulated Body Fluids.

Magnesium-based biodegradable metal bone implants exhibit superior mechanical properties compared to biodegradable polymers for orthopedic and cardiovascular stents. In this study, MgZZC-x (x = 1, 1.2) alloys were screened by in vitro biocompatibility tests in three simulated body fluids under nontoxic conditions. The MgZZC-1 alloys with better biocompatibility were selected to predict the days required for complete degradation. The evolution of degradation products was analyzed, and the mechanism of formation of the product film was inferred. A degradation kinetic model was established to investigate the effect of MEM components on the degradation of the alloys. The results demonstrate that the proteins in MEM can greatly retard the degradation progress by attaching to the surface of MgZZC-1 alloys, which are predicted to degrade completely within 341 days. The carbonate and phosphate buffers were adjusted to pH in MEM solution, delaying the degradation of magnesium alloys. This process in MEM more accurately reflects the actual degradation in the body and is superior to that in Hanks and SBF solutions. This study will promote the application of biodegradable materials in clinical medicine.

Enhancing specialization of attention-related EEG power and phase synchronism brain patterns by meditation.

Meditation, mental training that aims to improve one's ability to regulate their cognition, has been widely applied in clinical medicine. However, the mechanism by which meditation affects brain activity is still unclear. To explore this question, electroencephalogram data were recorded in 20 long-term meditators and 20 nonmeditators during 2 high-level cognitive tasks (meditation and mental calculation) and a relaxed resting state (control). Then, the power spectral density and phase synchronization of the electroencephalogram were extracted and compared between these 2 groups. In addition, machine learning was used to discriminate the states within each group. We found that the meditation group showed significantly higher classification accuracy and calculation efficiency than the control group. Then, during the calculation task, both the power and global phase synchronism of the gamma response decreased in meditators compared to their relaxation state; yet, no such change was observed in the control group. A potential explanation for our observations is that meditation improved the flexibility of the brain through neural plastic mechanism. In conclusion, we provided robust evidence that long-term meditation experience could produce detectable neurophysiological changes in brain activity, which possibly enhance the functional segregation and/or specialization in the brain.

C-Reactive Protein as an Early Marker of Severity and Outcome in Patients with SARS-CoV-2 Infection

Abstract Background SARS CoV-2 infection is a major cause of morbidity and mortality worldwide. The incidence of SARS CoV-2 infection is rising with millions of people getting infected daily. Mortality rate varies from a country to the other(0.3-6%), most of which are attributed to respiratory failure. Several studies have been performed to identify biomarkers for use in the early identification of patients at risk. Aim of the work This study was done to estimate and evaluate the plasma levels of CRP in patients with SARS CoV-2 and to study the association of CRP levels with severity of disease progression and prognosis of those patients. Patients and Methods This retrospective cohort study was conducted on 100 patients admitted to El-Obour Ain Shams University Specialized Hospital for Isolation during the period between January and October 2020. Results Regarding history patients whom were old or have any of the following comorbid conditons as hypertension, Diabetes mellitus, chronic liver disease and active cancer were statistically more prone to not surviving. Regarding complete blood picture, there was significant decrease in haemoglobin and or increase in total leukocytic count in those who did not survive. Serum Ferritin and D-Dimer were significantly high in those who did not survive. As regard CRP, it was significantly higher in the non-survivor group, Also it correlated with severity as ICU admission and need of respiratory support. Regarding the ROC curve result the best cut-off of CRP to predict mortality was &amp;gt;129mg/l, with Area under the curve = 0.981, Sensitivity = 90.91%, Specificity= 97.75%, positive predictive value = 83.3% and negative predictive value =98.9%. Regarding the ROC curve result the best cut-off of CRP to predict need of respiratory support was &amp;gt;55.3mg/L, with Area under the curve = 0.976, Sensitivity = 100%, Specificity= 86.21%, positive predictive value = 52.0% and negative predictive value =100.0%. Regarding the ROC curve result the best cut-off of CRP to predict need of ICU admission was &amp;gt;55.3mg/L, with Area under the curve = 0.987, Sensitivity = 100.0%, Specificity= 93.75%, positive predictive value = 80.0% and negative predictive value =100.0%. Conclusion Laboratory and clinical medicine groups should actively collaborate and optimize their individual expertise to potentially prove standardized biomarkers assays that will optimize patient care.

Navigating Disease Management: A Comprehensive Review of the De Ritis Ratio in Clinical Medicine.

The De Ritis ratio, defined as the serum aspartate aminotransferase (AST) to alanine aminotransferase (ALT) ratio, is a widely recognized biochemical marker with significant applications in diagnosing and managing various diseases, particularly liver disorders. This comprehensive review synthesizes current knowledge surrounding the clinical relevance of the De Ritis ratio, examining its historical development, diagnostic utility, and prognostic significance across various medical conditions, including liver diseases, cardiovascular disorders, and muscular pathologies. Through an in-depth analysis of literature spanning several decades, this review highlights the role of the De Ritis ratio not only in differential diagnosis but also as a prognostic indicator for disease progression and patient outcomes. The ratio's ability to distinguish between different types of liver pathology, aid in early disease detection, and its potential use in monitoring treatment response are discussed. Additionally, the review addresses the methodological considerations, such as confounding factors and interpretation challenges, that impact the clinical utility of the De Ritis ratio. Given the evolving landscape of clinical diagnostics and the push toward more personalized medicine, the review concludes with recommendations for further research. These include longitudinal studies to explore the ratio's changes over time, comparative research across diverse populations, and technological integration to enhance diagnostic accuracy and patient care. This review aims to reaffirm the importance of the De Ritis ratio in modern clinical practice and encourages continued exploration into its potential applications and benefits in healthcare.

Research on ultrasound-based radiomics: a bibliometric analysis.

A large number of studies related to ultrasound-based radiomics have been published in recent years; however, a systematic bibliometric analysis of this topic has not yet been conducted. In this study, we attempted to identify the hotspots and frontiers in ultrasound-based radiomics through bibliometrics and to systematically characterize the overall framework and characteristics of studies through mapping and visualization. A literature search was carried out in Web of Science Core Collection (WoSCC) database from January 2016 to December 2023 according to a predetermined search formula. Bibliometric analysis and visualization of the results were performed using CiteSpace, VOSviewer, R, and other platforms. Ultimately, 466 eligible papers were included in the study. Publication trend analysis showed that the annual publication trend of journals in ultrasound-based radiomics could be divided into three phases: there were no more than five documents published in this field in any year before 2018, a small yearly increase in the number of annual publications occurred between 2018 and 2022, and a high, stable number of publications appeared after 2022. In the analysis of publication sources, China was found to be the main contributor, with a much higher number of publications than other countries, and was followed by the United States and Italy. Frontiers in Oncology was the journal with the highest number of papers in this field, publishing 60 articles. Among the academic institutions, Fudan University, Sun Yat-sen University, and the Chinese Academy of Sciences ranked as the top three in terms of the number of documents. In the analysis of authors and cocited authors, the author with the most publications was Yuanyuan Wang, who has published 19 articles in 8 years, while Philippe Lambin was the most cited author, with 233 citations. Visualization of the results from the cocitation analysis of the literature revealed a strong centrality of the subject terms papillary thyroid cancer, biological behavior, potential biomarkers, and comparative assessment, which may be the main focal points of research in this subject. Based on the findings of the keyword analysis and cluster analysis, the keywords can be categorized into two major groups: (I) technological innovations that enable the construction of radiomics models such as machine learning and deep learning and (II) applications of predictive models to support clinical decision-making in certain diseases, such as papillary thyroid cancer, hepatocellular carcinoma (HCC), and breast cancer. Ultrasound-based radiomics has received widespread attention in the medical field and has been gradually been applied in clinical research. Radiomics, a relatively late development in medical technology, has made substantial contributions to the diagnosis, prediction, and prognostic evaluation of diseases. Additionally, the coupling of artificial intelligence techniques with ultrasound imaging has yielded a number of promising tools that facilitate clinical decision-making and enable the practice of precision medicine. Finally, the development of ultrasound-based radiomics requires multidisciplinary cooperation and joint efforts from the field biomedicine, information technology, statistics, and clinical medicine.

Designing Prefaculty Competencies for Diverse Learners Through a Modified Delphi Process

Faculty diversity in academic medicine may better prepare the next generation of equity-minded health care practitioners and leaders. Prefaculty development is an emerging concept to support trainees in achieving key knowledge, skills, and experiences to become successful faculty. To outline competencies, with corresponding milestones, to support the academic career development of learners, inclusive of racial, ethnic, sexual, and gender identities minoritized in medicine. Using a modified Delphi process, a national working group consisting of 13 members was established. The group used the published literature and listening sessions with diverse stakeholders to draft a set of competencies and milestones in July 2022. Diverse expert panelists reviewed the draft set over 2 rounds between September 2022 and January 2023. The group considered qualitative data to further refine the draft set between rounds. Consensus was reached when competencies and milestones were rated as agree or strongly agree on importance or appropriateness by 75% or greater of expert panelists after the second round. A final set of competencies and milestones was generated in February 2023. Data from round 1 were analyzed in October 2022 and data from round 2 were analyzed in January 2023. The development of prefaculty competencies with corresponding milestones by expert panel rankings and comments. The national working group consisted of 13 members who represented diversity across racial, ethnic, and gender identities and academic and career tracks. The working group developed an initial set of 36 competencies and corresponding milestones across 12 domains. After 2 rounds, consensus among 46 expert panelists generated a final list of 32 competencies with corresponding milestones across 11 domains. A total of 26 panelists (56.5%) were women, 11 (23.9%) were Black or African American, 17 (37.0%) were Latina/o/x/e, Hispanic, or of Spanish origin, and 10 (21.7%) were White. Competency domains were divided into 2 groups: foundational (academic career choice and professional identity, mentorship, networking, financial skills, diversity and inclusion, personal effectiveness and self-efficacy, and leadership) and focused (education, community engagement, research, and clinical medicine). Consensus for inclusion or elimination of items was greater than 90% between the 2 rounds. There was consensus among the working group and expert panelists regarding the importance and appropriateness of the competencies and milestones for diverse trainees to successfully obtain faculty positions. Institutions and national organizations can use these competencies as a framework to develop curricula that support diverse learners' career development toward academia.

Juxtaposition across Mathematical Modeling, Stochastic Processes along with the Lenses of Philosophy of Science and AI integration in Medicine and Biology: An Overview

The ultimate reason for the ubiquity of mathematics in modern science entails the essence of mathematical thinking and processes so that complex phenomena including those emerging in medical and biological systems can be understood, and thus, scientific models at their crux can be generated. The consequent complexities and uncertainties require the applications of stochastic processes in mathematical modeling with Artificial Intelligence (AI) techniques used in realms of medicine and biology. Within these conditions, clinical evaluation evidence and model explainability are considered to ensure accountable, effective and safe uses of AI in clinical settings, along with robust, reliable as well as accurate understanding of various complex processes that manifest huge numbers of heterogeneous temporospatial scales. The role of philosophy within science can be expounded by its juxtaposition with models and empirical data explicated by philosophy whose pillars are driven into semantic, pragmatic and syntactic structures of scientific theory that also make up the foundational tenets of algorithmic thinking and patterns. While philosophy of science examines and reflects on the concepts, theories, arguments and methods of science, it should also be borne in mind that scientific theory, by its definition, relates to applications validated by its predictions as units of analyses. Concerning mathematical models, their structure and behavior in target systems are also to be addressed so that explicit common patterns that are implicit in scientific practice can be included in this complex influx. On the other hand, critical functions of mathematical modeling from the pragmatic aspect include the unification of models and data, model fitting to the data, identification of mechanisms depending on observations as well as predictions of future observations. Given these, philosophy of science in medical and biological fields is stated to prompt a comprehensive understanding to construct holistic mathematical models particularly in complex sciences including different attributes of complexity, evolution and adaptation. Regarding the position of AI, its algorithms and mathematical modeling, the methods of neural networks, statistics, operations research, fractional calculus, fractals, and so forth can be employed with AI being capable of uncovering hidden insights embedded in big data concerning medical and biological issues in view of contemporary scientific thinking and processes. In addition, the treatment and handling of uncertainty in clinical medicine and biological problems over their processes may disclose compelling challenges due the fact that uncertainties are one of the intrinsic features of nearly all mathematical models which are formed based on three basic types of uncertainty: interval, Bayesian and stochastic. Accordingly, the current overview aims at providing answers built on sophisticated models considering the explanation and interpretation of design and formulation considering that the extant research literature could have some fragmented points in terms of original and application-oriented works. To these ends, the opportunities, challenges, limitations and conjunctures with respect to mathematical modeling in medicine and biology are addressed while role of philosophy of science is discussed within the context of mathematical modeling and applications in medicine and biology. In addition to these points, the delineation of forecasting, prediction, estimation and approximation concerning different mathematical modeling with the integration of AI in medicine and biology is explained. Thereby, an overview is inclusively presented by comprising the principles underpinning the medical and biological systems within a framework in relation to the diagnostic and disease-related treatment processes and follow-up, which can provide new directions in novel formulations, designs and interpretations based on mathematical modeling processes to be constructed and solved through practicality as well as to-the-point specific means.

Psychophysiological technologies using the biofeedback method: an analytical review

INTRODUCTION. There is high prevalence of post-stroke motor and neuropsychological disorders. However, they are usually considered without interrelation with each other, and rehabilitation methods are also considered in the context of a monodomain effect on specific functions. AIM. To analyze the available scientific evidence on the role of psychophysiological technologies with biofeedback in clinical medicine and medical rehabilitation. MATERIALS. 50 sources were analyzed, 25 of which are high-level evidence-based articles (randomized controlled trials, meta-analyses, systematic reviews) published in databases Elibrary.ru, Medline, Web of Science, PubMed and Scopus for the period of 2009–2024. RESULTS. The complex effect of the BFB method on the physical symptoms and psychological state of patients, as well as the possibility of using physiological indicators for adaptive biofeedback, has been studied. The greatest amount of evidence is available about the positive effect of technology using BFB on the psychoemotional state of patients and the state of the cardiovascular system. Significant results were obtained when using BFB-electromyographic-training in the complex therapy of headache and related emotional disorders. All studies have a high level of reliability of evidence and credibility of recommendations. Great progress has been made in the development of innovative psychophysiological technologies, including the use of hardware and software complexes, electroencephalography and neurointerfaces. CONCLUSION. Psychophysiological technologies with BFB demonstrate effectiveness in the rehabilitation of patients with psychosomatic disorders, borderline conditions and a number of neurological diseases (headache, epilepsy, spinal injury, stroke). Further research is needed to form an evidence base, clarify the mechanisms of action and algorithms of prescriptions, taking into account the physiological and psychological component of the therapeutic or rehabilitation process.

A Rapid Review of How Model‐based Systems Engineering is Used in Healthcare Systems

AbstractThis study presents the results from rapid review of how model‐based systems engineering (MBSE) is utilized in healthcare systems (HSs). We conduct a review of the last twelve years and find that MBSE adoption in HSs is accelerating, with use of various MBSE languages and tools, as well as their integration with other simulation and modeling techniques. We find that similar to engineered systems, the most common MBSE language is systems modeling language (SysML), followed by unified modeling language (UML) and others. Additionally, we observe that MBSE methods are frequently used in conjunction with other analytical techniques, such as simulation and co‐simulations, to analyze and enhance various HS operations, or to assist with making tradeoffs between HS attributes such as quality and cost. Moreover, we provide a non‐exhaustive classification of current research based on two dimensions: healthcare applications and MBSE use cases. Notably, MBSE is being implemented generally with patient‐centric objectives in various HS domains, including IoT‐enabled smart healthcare, clinical medicine, medical device development, healthcare process enhancement, and healthcare facilities management. While the primary MBSE use case involves modeling different aspects of healthcare operations, there is a significant number of studies that pursue requirements engineering, systems analysis, integration, verification and validation, as well as risk analysis and management. Furthermore, we identify two promising research gaps. First, there is a need for the integration of MBSE with state‐of‐the‐art data‐driven analytical methodologies such as hybrid simulation and artificial intelligence techniques. Second, HSs could greatly benefit from representing the cognitive functions and processes of human decision‐makers in the loop, such as healthcare providers (e.g., doctors and nurses), who are instrumental in sustaining the HS performance and functionality. We contend that MBSE and other SE methods and techniques could improve HSs design, operations, and management; while fostering resilience and long‐term sustainability.

Differences between human and artificial/augmented intelligence in medicine

The emphasis on artificial intelligence (AI) is rapidly increasing across many diverse aspects of society. This manuscript discusses some of the key topics related to the expansion of AI. These include a comparison of the unique cognitive capabilities of human intelligence with AI, and the potential risks of using AI in clinical medicine. The general public attitudes towards AI are also discussed, including patient perspectives. As the promotion of AI in high-risk situations such as clinical medicine expands, the limitations, risks and benefits of AI need to be better understood.

A Literature Review of the Integration of Ancient Indian Mythology in Clinical Medicine: A Holistic Approach to Health and Healing.

The fusion of mythology and ancient Indian medicine, particularly Ayurveda, is a fascinating synthesis of cultural heritage and scientific endeavor. Ayurveda encompasses a wide range of practices, including pharmacology, anatomy, physiology, surgery, and obstetrics, and integrates the rich tapestry of Hindu mythology, providing a comprehensive understanding of health and disease. The inclusion of mythological figures and narratives in the discourse of ancient Indian medicine offers a unique perspective on the integration of spiritual and empirical knowledge, highlighting the role of mythology in shaping the foundational principles of clinical medicine. The discourse explores the profound impact of Ayurveda and its mythological underpinnings on contemporary clinical practices, underscoring the timeless wisdom embedded in ancient narratives. These stories represent the bedrock of holistic medical practices, emphasizing the parity between mind, body, and spirit that is increasingly validated in modern therapeutic paradigms. The philosophy and methods detailed in the age-old texts of Sushruta and Charaka, coupled with the allegorical tales of Dhanvantari and Bharadwaja, contribute significantly to the foundational principles underpinning today's holistic medical approaches. The enduring legacy of Ayurveda and its mythological narratives continues to influence and inspire a holistic approach to health care, underscoring the indelible connection between ancient wisdom and modern medical practices.

A bioinspired switchable adhesive patch with adhesion and suction mechanisms for laparoscopic surgeries

Medical adhesives play an important role in clinical medicine because of their flexibility and convenient operation. However, they are still limited to laparoscopic surgeries, which have demonstrated urgent demand for liver retraction with minimal damage to the human body. Here, inspired by the suction cup structure of octopus, an adhesive patch with excellent mechanical properties, robust and switchable adhesiveness, and biocompatibility is proposed. The adhesive patch is combined by the attachment body mainly made of poly(acrylic acid) grafted with N-hydroxysuccinimide ester, crosslinked biodegradable gelatin methacrylate and biodegradable biopolymer gelatin to mimic the adhesive sucker rim, and the temperature-sensitive telescopic layer of microgel-crosslinked poly(N-isopropylacrylamide-co-2-hydroxyethyl methacrylate) to shrink and form internal cavity with reduced pressure. Through mechanical tests, adhesion evaluation, and biocompatibility analysis, the bioinspired adhesive patch has demonstrated its capacity not only in adhesion to tissues but also in potential treatment for medical applications, especially laparoscopic technology. The bioinspired adhesive patch can break through the limitations of traditional retraction methods, and become an ideal candidate for liver retraction in laparoscopic surgery and related clinical medicine.

Whole-Exome Sequencing: Is It a Panacea for Every Diagnostic Enigma in Clinical Medicine?

Whole-Exome Sequencing: Is It a Panacea for Every Diagnostic Enigma in Clinical Medicine?

High-fidelity CRISPR/Cas12a dual-crRNA screening reveals novel synergistic interactions in hepatocellular carcinoma.

: CRISPR/Cas12a-based combinational screening has shown remarkable potential for identifying genetic interactions. Here, we describe an innovative method for combinational genetic screening with rapid construction of a dual-CRISPR RNA (crRNA) library using gene splicing through overlap extension PCR (SOE PCR) and the adoption of CeCas12a, which we previously identified with strict PAM recognition and low off-targeting to guarantee fidelity and efficiency. The custom-pooled SOE crRNA array (SOCA) library for double-knockout screening could be conveniently constructed in the laboratory for widespread use, and the CeCas12a-mediated high-fidelity screen displayed good performance even under a negative selection screen. By designing a SOCA dual-crRNA library that covered most of the kinase and metabolism-associated gene targets of FDA-approved drugs implicated in hepatocellular carcinoma (HCC) tumourigenesis, novel cross-talk between the two gene sets was negatively selected to inhibit HCC cell growth in vitro and in vivo and was validated using virtual double-knockdown screening based on TCGA databases. Thus, this rapid, efficient and high-fidelity double-knockout screening system is promising for systemically identifying potential genetic interactions between multiple gene sets or combinations of FDA- approved drugs for clinical translational medicine in the future.