Role of a large plasmid of Salmonella typhi encoding multiple drug resistance

Twenty isolates of Salmonella typhi from cases of typhoid during the 1989-1990 epidemic in Calcutta were examined. Most isolates (84% of all isolates in the epidemic) were resistant to chloramphenicol, ampicillin, tetracycline and streptomycin but were sensitive to nalidixic acid and ciprofloxacin. Plasmids of 120 kb and 14 kb were identified amongst the multi-drug resistant isolates of S. typhi. However, there was no plasmid in the antibiotic-sensitive isolates. The 120-kb plasmid was transferable and transconjugants were resistant to chloramphenicol, ampicillin, tetracycline and streptomycin. Restriction endonuclease analysis patterns after EcoRI digestion of the 120-kb antibiotic-resistance plasmids from the S. typhi isolates and transconjugants were similar.     

Use of multiple antigenic peptides related to antigenic determinants of infectious bursal disease virus (IBDV) for detection of anti-IBDV-specific antibody in ELISA--quantitative comparison with native antigen for their use in serodiagnosis

Multiple antigenic peptides (MAPs) prepared for the predicted antigenic determinants on the VP2 protein of infectious bursal disease virus (IBDV) were used as antigens in enzyme-linked immunosorbent assay (ELISA)--an alternative to whole viral antigen to detect anti-IBDV antibodies in the chicken sera. Two MAPs were synthesized, which could specifically detect the anti-IBDV antibodies in serum samples by ELISA. The optimum quantity of MAP1 and MAP2 required to coat the wells of the ELISA plate was 5 ng/ml, whereas the amount of purified IBDV whole viral antigen was 500 ng/ml, indicating the high efficiency of MAPs. In this study, we mainly focused on the antigenicity of two eight-branched MAPs to detect anti-IBDV antibodies in ELISA, which would serve as safe, chemically defined, noninfectious alternative antigens to whole virus in serodiagnosis. The specificity and sensitivity of both MAP1 and MAP2 were found to be relatively better than the whole viral antigen.     

Sertoli-Leydig cell tumor of ovary with heterologous element: a case report

Sertoli-Leydig cell tumors are uncommon tumors of ovary accounting for about 1% of sex cord stromal tumors. They constitute between 0.1% to 0.5% of all the primary ovarian neoplasms. The majority of reported cases are moderately or poorly differentiated combined Sertoli-Leydig cell tumors. A rare case of Sertoli-Leydig cell tumor with heterologous element in a 14 year girl is described. The heterologous component comprised smooth muscle, an uncommon element, that was seen in 60 to 70% of the tumor area. The epithelial element of the tumor consisted of solid, tubular and foci of retiform pattern. Both these features imply a poor prognosis.     

Impact of a Mental Health Based Primary Care Program on Quality of Physical Health Care

We examine the impact of mental health based primary care on physical health treatment among community mental health center patients in New York State using propensity score adjusted difference in difference models. Outcomes are quality indicators related to outpatient medical visits, diabetes HbA1c monitoring, and metabolic monitoring of antipsychotic treatment. Results suggest the program improved metabolic monitoring for patients on antipsychotics in one of two waves, but did not impact other quality indicators. Ceiling effects may have limited program impacts. More structured clinical programs to may be required to achieve improvements in quality of physical health care for this population.     

Arthroscopic-assisted fixation of Hoffa fracture: A case report and description of technique

Medial condyle Hoffa fractures are intra articular injuries, which require prompt anatomical reduction and internal fixation. Arthroscopy-guided fixation of these fractures has also been advocated; the advantages include avoidance of soft tissue dissection, fast recovery and mobilization. We describe a case of a 28-year-old male patient presenting with a 10-day-old medial condylar Hoffa fracture. Skin condition prevented from conventional open reduction and internal fixation; hence, taking note of the urgency of fixation of an intraarticular fracture, arthroscopic-assisted minimally invasive approach was performed with good outcome.     

Artificial intelligence in brachytherapy: a summary of recent developments

Artificial intelligence (AI) applications, in the form of machine learning and deep learning, are being incorporated into practice in various aspects of medicine, including radiation oncology. Ample evidence from recent publications explores its utility and future use in external beam radiotherapy. However, the discussion on its role in brachytherapy is sparse. This article summarizes available current literature and discusses potential uses of AI in brachytherapy, including future directions. AI has been applied for brachytherapy procedures during almost all steps, starting from decision-making till treatment completion. AI use has led to improvement in efficiency and accuracy by reducing the human errors and saving time in certain aspects. Apart from direct use in brachytherapy, AI also contributes to contemporary advancements in radiology and associated sciences that can affect brachytherapy decisions and treatment. There is a renewal of interest in brachytherapy as a technique in recent years, contributed largely by the understanding that contemporary advances such as intensity modulated radiotherapy and stereotactic external beam radiotherapy cannot match the geometric gains and conformality of brachytherapy, and the integrated efforts of international brachytherapy societies to promote brachytherapy training and awareness. Use of AI technologies may consolidate it further by reducing human effort and time. Prospective validation over larger studies and incorporation of AI technologies for a larger patient population would help improve the efficiency and acceptance of brachytherapy. The enthusiasm favoring AI needs to be balanced against the short duration and quantum of experience with AI in limited patient subsets, need for constant learning and re-learning to train the AI algorithms, and the inevitability of humans having to take responsibility for the correctness and safety of treatments.