DrugMap: A quantitative pan-cancer analysis of cysteine ligandability.

Cysteine-focused chemical proteomic platforms have accelerated the clinical development of covalent inhibitors for a wide range of targets in cancer. However, how different oncogenic contexts influence cysteine targeting remains unknown. To address this question, we have developed "DrugMap," an atlas of cysteine ligandability compiled across 416 cancer cell lines. We unexpectedly find that cysteine ligandability varies across cancer cell lines, and we attribute this to differences in cellular redox states, protein conformational changes, and genetic mutations. Leveraging these findings, we identify actionable cysteines in NF-κB1 and SOX10 and develop corresponding covalent ligands that block the activity of these transcription factors. We demonstrate that the NF-κB1 probe blocks DNA binding, whereas the SOX10 ligand increases SOX10-SOX10 interactions and disrupts melanoma transcriptional signaling. Our findings reveal heterogeneity in cysteine ligandability across cancers, pinpoint cell-intrinsic features driving cysteine targeting, and illustrate the use of covalent probes to disrupt oncogenic transcription-factor activity.

DrugMap: A quantitative pan-cancer analysis of cysteine ligandability.

Cysteine-focused chemical proteomic platforms have accelerated the clinical development of covalent inhibitors of a wide-range of targets in cancer. However, how different oncogenic contexts influence cysteine targeting remains unknown. To address this question, we have developed DrugMap , an atlas of cysteine ligandability compiled across 416 cancer cell lines. We unexpectedly find that cysteine ligandability varies across cancer cell lines, and we attribute this to differences in cellular redox states, protein conformational changes, and genetic mutations. Leveraging these findings, we identify actionable cysteines in NFκB1 and SOX10 and develop corresponding covalent ligands that block the activity of these transcription factors. We demonstrate that the NFκB1 probe blocks DNA binding, whereas the SOX10 ligand increases SOX10-SOX10 interactions and disrupts melanoma transcriptional signaling. Our findings reveal heterogeneity in cysteine ligandability across cancers, pinpoint cell-intrinsic features driving cysteine targeting, and illustrate the use of covalent probes to disrupt oncogenic transcription factor activity.

The Utilization of Machine Learning Algorithms for Assisting Physicians in the Diagnosis of Diabetes.

This paper investigates the use of machine learning algorithms to aid medical professionals in the detection and risk assessment of diabetes. The research employed a dataset gathered from individuals with type 2 diabetes in Ninh Binh, Vietnam. A variety of classification algorithms, including Decision Tree Classifier, Logistic Regression, SVC, Ada Boost Classifier, Gradient Boosting Classifier, Random Forest Classifier, and K Neighbors Classifier, were utilized to identify the most suitable algorithm for the dataset. The results of the present study indicate that the Random Forest Classifier algorithm yielded the most promising results, exhibiting a cross-validation score of 0.998 and an accuracy rate of 100%. To further evaluate the effectiveness of the selected model, it was subjected to a testing phase involving a new dataset comprising 67 patients that had not been previously seen. The performance of the algorithm on this dataset resulted in an accuracy rate of 94%, especially the study's notable finding is the algorithm's accurate prediction of the probability of patients developing diabetes, as indicated by the class 1 (diabetes) probabilities. This innovative approach offers a meticulous and quantifiable method for diabetes detection and risk evaluation, showcasing the potential of machine learning algorithms in assisting clinicians with diagnosis and management. By communicating the diabetes score and probability estimates to patients, the comprehension of their disease status can be enhanced. This information empowers patients to make informed decisions and motivates them to adopt healthier lifestyle habits, ultimately playing a crucial role in impeding disease progression. The study underscores the significance of leveraging machine learning in healthcare to optimize patient care and improve long-term health outcomes.

Fabrication of a Low-Cost Microfluidic Device for High-Throughput Drug Testing on Static and Dynamic Cancer Spheroid Culture Models.

Drug development is a complex and expensive process from new drug discovery to product approval. Most drug screening and testing rely on in vitro 2D cell culture models; however, they generally lack in vivo tissue microarchitecture and physiological functionality. Therefore, many researchers have used engineering methods, such as microfluidic devices, to culture 3D cells in dynamic conditions. In this study, a simple and low-cost microfluidic device was fabricated using Poly Methyl Methacrylate (PMMA), a widely available material, and the total cost of the completed device was USD 17.75. Dynamic and static cell culture examinations were applied to monitor the growth of 3D cells. α-MG-loaded GA liposomes were used as the drug to test cell viability in 3D cancer spheroids. Two cell culture conditions (i.e., static and dynamic) were also used in drug testing to simulate the effect of flow on drug cytotoxicity. Results from all assays showed that with the velocity of 0.005 mL/min, cell viability was significantly impaired to nearly 30% after 72 h in a dynamic culture. This device is expected to improve in vitro testing models, reduce and eliminate unsuitable compounds, and select more accurate combinations for in vivo testing.

Lower Extremity Nerve Conduction Abnormalities in Vietnamese Patients with Type 2 Diabetes: A Cross-Sectional Study on Peripheral Neuropathy and Its Correlation with Glycemic Control and Renal Function.

Peripheral neuropathy is a common complication of type 2 diabetes mellitus (T2DM) that results in nerve conduction abnormalities. This study aimed to investigate the parameters of nerve conduction in lower extremities among T2DM patients in Vietnam. A cross-sectional study was conducted on 61 T2DM patients aged 18 years and older, diagnosed according to the American Diabetes Association's criteria. Data on demographic characteristics, duration of diabetes, hypertension, dyslipidemia, neuropathy symptoms, and biochemical parameters were collected. Nerve conduction parameters were measured in the tibial and peroneal nerves, including peripheral motor potential time, response amplitude M, and motor conduction speed, as well as sensory conduction in the shallow nerve. The study found a high rate of peripheral neuropathy among T2DM patients in Vietnam, with decreased conduction rate, motor response amplitude, and nerve sensation. The incidence of nerve damage was highest in the right peroneal nerve and left peroneal nerve (86.7% for both), followed by the right tibial nerve and left tibial nerve (67.2% and 68.9%, respectively). No significant differences were found in the rate of nerve defects between different age groups, body mass index (BMI) groups, or groups with hypertension or dyslipidemia. However, a statistically significant association was found between the rate of clinical neurological abnormalities and the duration of diabetes (p < 0.05). Patients with poor glucose control and/or decreased renal function also had a higher incidence of nerve defects. The study highlights the high incidence of peripheral neuropathy among T2DM patients in Vietnam and the association between nerve conduction abnormalities and poor glucose control and/or decreased renal function. The findings underscore the importance of early diagnosis and management of neuropathy in T2DM patients to prevent serious complications.

Synthesis of glycyrrhetinic acid-modified liposomes to deliver Murrayafoline A for treatment of hepatocellular carcinoma.

Hepatocellular carcinoma is a common type of cancer associated with a high mortality rate. Among several bioactive compounds, Murrayafoline A (MuA) has been proved as a bio substance that exhibits great potentials in treating liver cancer. In order to overcome the high cytotoxicity and low solubility of MuA, a delivery system based on nanocarriers is necessary to deliver MuA towards the desired target. In the present study, 18ß-glycyrrhetinic acid (GA), which is known as a ligand for liver targeting, was used to construct the cholesterol-poly (ethylene glycol)-glycyrrhetinic acid (GA-PEG-Chol) conjugate and liposome for MuA administration. The compound was then examined for therapeutic efficacy and safety in HUVEC and HepG2 cells in 2D and 3D cell cultures. Results have shown that MuA-loaded liposomes had IC50 value of 2 µM in HepG2 and had the cytosolic absorption of 8.83 ± 0.97 ng/105 cells, while The IC50 value of MuA-loaded liposomes in HUVEC cell lines was 15 µM and the the cytosolic absorption was recorded as 3.62 ± 0.61 cells. The drug test on the 3D cancer sphere platform of the HepG2 cancer sphere showed that MuA-loaded GA liposomes had the highest efficacy at a concentration of 100 µg/mL. In short, these results suggest that MuA-loaded GA liposomes have the potential for maintenance drug delivery and liver targeting.

A low-cost, flexible extruder for liposomes synthesis and application for Murrayafoline A delivery for cancer treatment.

Liposomal encapsulation is a drug delivery strategy with many advantages, such as improved bioavailability, ability to carry large drug loads, as well as controllability and specificity towards various targeted diseased tissues. Currently, most preparation techniques require an additional extrusion or filtering step to obtain monodisperse liposomes with the size of less than 100 nm. In this study, a compact liposome extruder was designed at a cost of $4.00 and used to synthesize liposome suspensions with defined particle size and high homogeneity for Murrayafoline A (Mu-A) loading and release. The synthesized MuA-loaded liposomes displayed a biphasic drug release and remained stable under the storage condition of 4°C. They also significantly reduced the viability of HepG2 cells in the cancer spheroids by 25%. The low-cost, flexible liposome extruder would allow the researchers to study liposomes and their applications in a cost-effective manner.

Gene expression, chromosome heterogeneity and the fast-X effect in mammals.

The higher rate of non-synonymous over synonymous substitutions (dN/dS) of the X chromosome compared with autosomes is often interpreted as a consequence of X hemizygosity. However, other factors, such as gene expression, are also known to vary between X and autosomes. Analysing 4800 orthologues in six mammals, we found that gene expression levels, associated with GC content, fully account for the variation in dN/dS between X and autosomes with no detectable effect of hemizygosity. We also report an extensive variance in dN/dS and gene expression between autosomes.