Seminal fluid is the secretion from many glands comprised of several organic and inorganic compounds including free amino acids, proteins, fructose, glucosidase, zinc, and other scavenging elements like Mg²⁺, Ca²⁺, K⁺, and Na⁺. Therefore, in the view of development of novel approaches and proper diagnosis to male infertility, overall understanding of the biochemical and molecular composition and its role in regulation of sperm quality is highly desirable. Perhaps this can be achieved through artificial intelligence. This study was aimed to elucidate and predict various biochemical markers present in human seminal plasma with three different neural network models. A total of 177 semen samples were collected for this research (both fertile and infertile samples) and immediately processed to prepare a semen analysis report, based on the protocol of the World Health Organization (WHO [2010]). The semen samples were then categorized into oligoasthenospermia (n=35), asthenospermia (n=35), azoospermia (n=22), normospermia (n=34), oligospermia (n=34), and control (n=17). The major biochemical parameters like total protein content, fructose, glucosidase, and zinc content were elucidated by standard protocols. All the biochemical markers were predicted by using three different artificial neural network (ANN) models with semen parameters as inputs. Of the three models, the back propagation neural network model (BPNN) yielded the best results with mean absolute error 0.025, -0.080, 0.166, and -0.057 for protein, fructose, glucosidase, and zinc, respectively. This suggests that BPNN can be used to predict biochemical parameters for the proper diagnosis of male infertility in assisted reproductive technology (ART) centres. Abbreviations: AAS: absorption spectroscopy; AI: artificial intelligence; ANN: artificial neural networks; ART: assisted reproductive technology; BPNN: back propagation neural network model; DT: decision tress; MLP: multilayer perceptron; PESA: percutaneous epididymal sperm spiration; RBFN: radical basis function network; SRNN: simple recurrent neural network; SVM: support vector machines; TSE: testicular sperm extraction; WHO: World Health Organization

An extracellular β-glucosidase was isolated from Proteus mirabilis VIT117 found to be growing on prawn shells. The enzyme production was found to be enhanced (14.58 U/ml) when the culture was maintained at pH 9 and provided with sorbitol as carbon source, yeast extract as nitrogen source and incubated at 37 °C for approximately 72 h. Statistical methods like Plackett–Burman and RSM were also applied here to study the effects of different combinations of growth parameters for the bacteria, where the most significant parameters were found to be inoculum size, pH, yeast extract, incubation time and sorbitol. The optimum concentrations of inoculum size, pH and yeast extract determined by RSM were 2 %, 9 and 2 %, respectively. Partial purification of the protein was done by ammonium sulfate precipitation, followed by dialysis, gel filtration chromatography and SDS-PAGE. The enzyme was found to have a molecular weight of approximately 50 kDa and was observed to be most active at 37 °C in pH 9, with a sharp decline in the enzyme activity when temperature or the pH was increased. Enzyme kinetics study was performed to understand the catalytic behavior of the enzyme and it was found that our β-glucosidase had 5.613 U/ml and 0.082 mM as V _max and K _m values, respectively.