Background and Objectives:The availability of baseline information on the epidemiology of sexually transmitted infections (STIs) and other associated risk behaviors is essential for designing, implementing, and monitoring successful targeted interventions. Also, continuous analysis of risk assessment and prevalence-based screening studies are necessary to evaluate and monitor the performance of syndromic management. The aim of the present study was to document the pattern of common STIs and to evaluate the performance of syndromic case management against their laboratory diagnoses.Materials and Methods:Three hundred consecutive patients who attended the STI clinic of a tertiary care hospital at Delhi, with one or more of the complaints as enunciated by WHO in its syndromic approach for the diagnosis of STIs, were included as subjects. Detailed history, demographical data, and clinical features were recorded and screened for common STIs by standard microbiological methods.Results:The mean age was 24 years and most of the male patients were promiscuous and had contact with commercial sex workers (CSWs 63.9%). Majority came with the complaint of genital discharge (63 males; 54 females) followed by genital ulcer (61 males; 30 females). Genital herpes accounted for the maximum number of STI (86/300) followed by syphilis (71/300). The sensitivity of genital discharge syndrome (GDS) was high for Neisseria gonorrhoeae and Chlamydia trachomatis (96% and 91%, respectively) while specificity was low (76% and 72%, respectively). The sensitivity of genital ulcer syndrome for herpes simplex virus-2 (HSV-2) and Treponema pallidum was 82.65% and 81.2%, respectively, while specificity reached 99% approximately.Conclusions:Viral STIs constitute the major burden of the STI clinic and enhance the susceptibility of an individual to acquire or transmit HIV through sexual contact. Syndromic algorithms have some shortcomings, and they need to be periodically reviewed and adapted to the epidemiological patterns of STI in a given setting.

Unusual fungal agents that exist environmentally as saprophytes can often lead to opportunistic infections. Hyalohyphomycosis is a group of fungal infections caused by fungi characterized by hyaline septate hyphae and can infect both immunocompetent as well as immunocompromised patients. Many a times it becomes difficult to distinguish a pathogenic and a contaminant fungus, because many such agents can assume clinical significance depending on circumstances. Subcutaneous and invasive fungal infection due to the emerging hyalohyphomycotic fungus, Acremonium, has drawn the attention of clinicians and microbiologists, as a potential pathogen in patients with and without underlying risk factors. Generally considered to be minimally invasive in the past, genus Acremonium has been responsible for eumycotic mycetomas and focal infections in otherwise healthy individuals. It has also been increasingly implicated in systemic fungal diseases. The management with different antifungals in various clinical situations has been very conflicting and hence needs to be carefully evaluated. This overview is an endeavor to consolidate the available clinical infections due to Acremonium and the recommendations on treatment.

OBJECTIVESAedes mosquitoes are responsible for transmitting the dengue virus. The mosquito lifecycle is known to be influenced by temperature, rainfall, and relative humidity. This retrospective study was planned to investigate whether climatic factors could be used to predict the occurrence of dengue in East Delhi.METHODSThe number of monthly dengue cases reported over 19 years was obtained from the laboratory records of our institution. Monthly data of rainfall, temperature, and humidity collected from a local weather station were correlated with the number of monthly reported dengue cases. One-way analysis of variance was used to analyse whether the climatic parameters differed significantly among seasons. Four models were developed using negative binomial generalized linear model analysis. Monthly rainfall, temperature, humidity, were used as independent variables, and the number of dengue cases reported monthly was used as the dependent variable. The first model considered data from the same month, while the other three models involved incorporating data with a lag phase of 1, 2, and 3 months, respectively.RESULTSThe greatest number of cases was reported during the post-monsoon period each year. Temperature, rainfall, and humidity varied significantly across the pre-monsoon, monsoon, and post-monsoon periods. The best correlation between these three climatic factors and dengue occurrence was at a time lag of 2 months.CONCLUSIONSThis study found that temperature, rainfall, and relative humidity significantly affected dengue occurrence in East Delhi. This weather-based dengue empirical model can forecast potential outbreaks 2-month in advance, providing an early warning system for intensifying dengue control measures.