MATrix LABoratory Algorithm in Ultrasound Image-Guided General Drug Anesthesia along with Lumbar and Sacral Plexus Block in Hip Replacement under

Objective. The study aimed to explore the application of ultrasound image-guided general drug anesthesia combined with lumbar and sacral plexus block based on MATrix LABoratory (MATLAB) algorithm in hip arthroplasty and to study its clinical effect. Methods. The classic geodesic active contour (GAC) algorithm and the improved fuzzy clustering level set algorithm were used to segment ultrasound images of waist plexus, and then their segmentation effects were compared. Both algorithms are from the MATrix LABoratory (MATLAB) platform. A total of 60 patients undergoing hip arthroplasty were selected and randomly enrolled into control and experimental groups. The control group accepted general drug anesthesia, and the experimental group accepted ultrasound-guided lumbar and sacral plexus block combined with general anesthesia. The mean arterial pressure and heart rate at t0 (before anesthesia), t1 (before ventilation), t2 (when the skin was incised), t3 (when the prosthesis was implanted), t4 (when the incision was closed), and t5 (at the end of ventilation) were observed, and the intraoperative sufentanil dosage and 24 h analgesic dosage, the incidence of postoperative delirium, and the incidence of cognitive dysfunction were recorded. Results. The improved fuzzy clustering level set algorithm was better than the GAC model algorithm in image segmentation and running time. In contrast with the control group, the average arterial pressure and heart rate of the experimental group at the four time points of t1, t2, t3, and t5 were obviously reduced ( P < 0.05). In contrast with the control group, the amount of sufentanil and analgesics in the experimental group was obviously reduced ( P < 0.05), and the incidence of postoperative cognitive dysfunction and delirium was obviously reduced ( P < 0.05). Conclusion. The improved fuzzy clustering level set algorithm is superior to the GAC model in image segmentation and running time. Under its guidance, the lumbar and sacral plexus block combined with general anesthesia has a good clinical effect in hip arthroplasty, which is better than simple general anesthesia.

Interactions of anti-COVID-19 drug candidates with hepatic transporters may cause liver toxicity and affect pharmacokinetics

Transporters in the human liver play a major role in the clearance of endo- and xenobiotics. Apical (canalicular) transporters extrude compounds to the bile, while basolateral hepatocyte transporters promote the uptake of, or expel, various compounds from/into the venous blood stream. In the present work we have examined the in vitro interactions of some key repurposed drugs advocated to treat COVID-19 (lopinavir, ritonavir, ivermectin, remdesivir and favipiravir), with the key drug transporters of hepatocytes. These transporters included ABCB11/BSEP, ABCC2/MRP2, and SLC47A1/MATE1 in the canalicular membrane, as well as ABCC3/MRP3, ABCC4/MRP4, SLC22A1/OCT1, SLCO1B1/OATP1B1, SLCO1B3/OATP1B3, and SLC10A1/NTCP, residing in the basolateral membrane. Lopinavir and ritonavir in low micromolar concentrations inhibited BSEP and MATE1 exporters, as well as OATP1B1/1B3 uptake transporters. Ritonavir had a similar inhibitory pattern, also inhibiting OCT1. Remdesivir strongly inhibited MRP4, OATP1B1/1B3, MATE1 and OCT1. Favipiravir had no significant effect on any of these transporters. Since both general drug metabolism and drug-induced liver toxicity are strongly dependent on the functioning of these transporters, the various interactions reported here may have important clinical relevance in the drug treatment of this viral disease and the existing co-morbidities.

Drugs repurposed for COVID-19 by virtual screening of 6,218 drugs and cell-based assay

The COVID-19 pandemic caused by SARS-CoV-2 is an unprecedentedly significant health threat, prompting the need for rapidly developing antiviral drugs for the treatment. Drug repurposing is currently one of the most tangible options for rapidly developing drugs for emerging and reemerging viruses. In general, drug repurposing starts with virtual screening of approved drugs employing various computational methods. However, the actual hit rate of virtual screening is very low, and most of the predicted compounds are false positives. Here, we developed a strategy for virtual screening with much reduced false positives through incorporating predocking filtering based on shape similarity and postdocking filtering based on interaction similarity. We applied this advanced virtual screening approach to repurpose 6,218 approved and clinical trial drugs for COVID-19. All 6,218 compounds were screened against main protease and RNA-dependent RNA polymerase of SARS-CoV-2, resulting in 15 and 23 potential repurposed drugs, respectively. Among them, seven compounds can inhibit SARS-CoV-2 replication in Vero cells. Three of these drugs, emodin, omipalisib, and tipifarnib, show anti-SARS-CoV-2 activities in human lung cells, Calu-3. Notably, the activity of omipalisib is 200-fold higher than that of remdesivir in Calu-3. Furthermore, three drug combinations, omipalisib/remdesivir, tipifarnib/omipalisib, and tipifarnib/remdesivir, show strong synergistic effects in inhibiting SARS-CoV-2. Such drug combination therapy improves antiviral efficacy in SARS-CoV-2 infection and reduces the risk of each drug’s toxicity. The drug repurposing strategy reported here will be useful for rapidly developing drugs for treating COVID-19 and other viruses.

Prediction of Drug Users Based on Facial Scratching Pattern

The current practice of drug inspection is usually carried out at school or university. This procedure, however, is not effective and efficient, as the urine samples are taken randomly. In many cases, the drug-taking student is not present or evades the urine or hair inspection. A predictive drug user tool is needed, where only suspected student drug users are selected for a urine test. In general, drug abuse constantly causes terrible damage to the skin lesions Since they damage the skin during hallucinations due to the effects of drugs. The Grey Level of Occurrence Matrix (GLCM) is used in this study to discover the scratch pattern. Our proposed GLCM is evaluated with 104 images collected from the Internet. Training data is generated from 88 images of people before and after the drug was collected from the Internet, and we set 16 image faces to test the prediction. The experiment shows that the prediction based on GLCM has better accuracy (81%) compared with the local binary pattern (LBP) which only reach up to 75%.

Instant Hydrogelation Encapsulates Drug onto Implants Intraoperatively against Osteoarticular Tuberculosis

Osteoarticular Tuberculosis (TB) is a challenging issue because of its chronicity and recurrence. Many drug release systems have been developed for the general chemotherapy. Different from the general drug-release system,...

Are Hsp90 inhibitors good candidates against Covid-19?

: Drug reposition, or repurposing, has become a promising strategy in therapeutics due to its advantages in several aspects of drug therapy. General drug development is expensive and can take more than 10 years to go through the designing, development, and necessary approval steps. However, established drugs have already overcome these steps and thus a potential candidate may be already available decreasing the risks and costs involved. Viruses invade cells, usually provoking biochemical changes, leading to tissue damage, alteration of normal physiological condition in organisms and can even result in death. Inside the cell, the virus finds the machinery necessary for its multiplication, as for instance the protein quality control system, which involves chaperones and Hsps (heat shock proteins) that, in addition to physiological functions, help in the stabilization of viral proteins. Recently, many inhibitors of Hsp90 have been developed as therapeutic strategies against diseases such as the Hsp90 inhibitors used in anticancer therapy. Several shreds of evidence indicate that these inhibitors can also be used as therapeutic strategies against viruses. Therefore, since a drug treatment for COVID-19 is urgently needed, this review aims to discuss the potential use of Hsp90 inhibitors in the treatment of this globally threatening disease.

Implementation of the regulation on prescription for health insurance out-patients in Ninh Hoa Regional Hospital, 2019

Objectives: The study assesses the current status of implementation of outpatient prescriptions and analyze some influencing factors at Ninh Hoa Regional General Hospital in 2019. Results: The results show that: 94, 75% of the prescription are safe; 29.75% prescription with 1 to 2 antibiotics; The average number of drugs in 1 prescription is 3.7 ± 1.24 medicines; The percentage of prescriptions with general drug interaction is 5.25%. There is no fourth level drug interaction. Some influencing factors are: Doctors do not pay adequate attention to safe and rational prescription. The bidding for drugs, the provision and supply of drugs are still delayed in time for medical examination and treatment. Hospitals do not have software for searching drug information and drug interactions Conclusion: Doctors should regularly update the drug information, comply with the prescription regulations, and avoid interactions when prescribing. The pharmacy department should advise the hospital director to sign a close contract with the pharmaceutical companies to supply drugs to patients fully and promptly. Key words: prescription, Regulation on Prescription, Health Insurance, Outpatient

Artificial Intelligence Based Study on Analyzing of Habits and with History of Diseases of Patients for Prediction of Recurrence of Disease Due to COVID-19

A patient will visit physicians when he/she feels ill. This illness is not for COVID-19 but it is a general tendency of human being to visit doctor probably it can not be controlled by general drug. When a patient comes to a doctor, the doctor examines him/her after knowing his/her problem. The physician always asks him/her about some questions related to him/her daily life. For example, if a young male patient comes to a doctor with a symptom of fever and cough, the first question doctor asked him that he has a habit of smoking. Then doctor asks him whether this type of symptom appeared often to him previously or not. If the answers of both questions are yes, then the first one is habit and the second one is that he may suffering from some serious disease or a disease due to the weather. The aim of this paper is to consider habit of the patient as well as he/she has been affected by a critical disease. This information is used to build a model that will predict whether there is any possibility of his/her being affected by COVID-19. This research work contributes to tackle the pandemic situation occurred due to Corona Virus Infectious Disease, 2019 (Covid-19). Outbreak of this disease happens based on numerous factors such as past health records and habits of patients. Health records include diabetes tendency, cardiovascular disease existence, pregnancy, asthma, hypertension, pneumonia; chronic renal disease may contribute to this disease occurrence. Past lifestyles such as tobacco, alcohol consumption may be analyzed. A deep learning based framework is investigated to verify the relationship between past health records, habits of patients and covid-19 occurrence. A stacked Gated Recurrent Unit (GRU) based model is proposed in this paper that identifies whether a patient can be infected by this disease or not. The proposed predictive system is compared against existing benchmark Machine Learning classifiers such as Support Vector Machine (SVM) and Decision Tree (DT).

Review of Drug Utilization Studies in Neonatal Units: A Global Perspective

Rational prescribing is challenging in neonatology. Drug utilization studies help identify and define the problem. We performed a review of the literature on drug use in neonatal units and describe global variations. We searched databases (EMBASE, CINAHL and Medline) from inception to July 2020, screened studies and extracted relevant data (two reviewers). The search revealed 573 studies of which 84 were included. India (n = 14) and the USA (n = 13) reported the most. Data collection was prospective (n = 56) and retrospective (n = 26), mostly (n = 52) from one center only. Sixty studies described general drug use in 34 to 450,386 infants (median (IQR) 190 (91–767)) over a median (IQR) of 6 (3–18) months. Of the participants, 20–87% were preterm. The mean number of drugs per infant (range 11.1 to 1.7, pooled mean (SD) 4 (2.4)) was high with some reporting very high burden (≥30 drugs per infant in 8 studies). This was not associated with the proportion of preterm infants included. Antibiotics were the most frequently used drug. Drug use patterns were generally uniform with some variation in antibiotic use and more use of phenobarbitone in Asia. This study provides a global perspective on drug utilization in neonates and highlights the need for better quality information to assess rational prescribing.