Testosterone therapy in men with Parkinson disease : results of the TEST-PD study

Background: Testosterone deficiency has been reported in patients with Parkinson disease (PD), Alzheimer disease, and Huntington disease. It is not known whether testosterone therapy (TT) in men with borderline hypogonadism and neurodegenerative diseases will be of substantial benefit. Previously, we reported that testosterone deficiency is more common in patients with PD compared with age-matched control subjects, and we also reported in 2 small open-label studies that some nonmotor symptoms responded favorably to TT. Objective To define the effects of TT on nonmotor and motor symptoms in men with PD and probable testosterone deficiency. Design: Double-masked, placebo-controlled, parallel-group, single-center trial. Patients: Two experimental groups: patients with PD who were receiving either TT or placebo. Interventions: Participants received either the study drug by intramuscular injection (200 mg/mL of testosterone enanthate every 2 weeks for 8 weeks) or placebo (isotonic sodium chloride solution injections). In patients in each group, the testosterone serum concentration was obtained at each study visit. During 2 study visits, testosterone levels were blindly evaluated and the intramuscular testosterone dose was increased by 200 mg/mL if the free testosterone value failed to double from the baseline value. Main Outcome Measures: The primary outcome variable was the St Louis Testosterone Deficiency Questionnaire, and secondary outcome measures included measures of mood, cognition, fatigue, motor function, and frequency of adverse events. At the end of the double-blind phase, all patients were offered open-label TT and were followed up after 3 and 6 months. Results: Fifteen patients in the placebo group (mean age, 69.9 years), receiving a mean total levodopa equivalent dose of 924 mg/d, had a baseline free testosterone level of 47.91 pg/mL, compared with 15 patients in the TT group (mean age, 66.7 years), receiving an average total levodopa equivalent dose of 734 mg/d, who had a baseline free testosterone level of 63.49 pg/mL. Testosterone was generally well tolerated. More subjects in the TT group experienced lower extremity edema (40% vs 20%). In 2 patients, 1 in each group, prostate-specific antigen levels were elevated from baseline. The improvement in the TT group compared with the placebo group (1.7 vs 1.1) on the St Louis Testosterone Deficiency Scale was not statistically significant. In addition, there were no significant differences in motor and nonmotor features of PD between the 2 groups, although a few subscales showed improvements (Hopkins Verbal Learning Test, P<.04; and Backward Visual Span subtrial, P<.03). However, long-term open-label TT resulted in delayed but sustained improvement in subjects in the TT group who continued to receive treatment (n = 6) compared with subjects in the placebo group who elected not to receive TT (n = 3). Conclusions: Testosterone therapy was generally well tolerated in elderly men with PD and probable testosterone deficiency. While there was no significant difference in the motor and nonmotor scales between the TT and placebo groups at the end of 8 weeks compared with baseline, this may be due to several study limitations, including small sample size, a strong placebo effect with intramuscular therapy, and short follow-up that did not allow measurement of delayed effects of TT in some subjects. Until more definitive studies are reported, practitioners should be particularly cautious in treatment of low testosterone concentrations in men with PD and borderline testosterone deficiency, and careful consideration should be given to the risks vs the benefits of TT

Early prediction of COVID-19 outcome using artificial intelligence techniques and only five laboratory indices

We aimed to develop a prediction model for intensive care unit (ICU) hospitalization of Coronavirus disease-19 (COVID-19) patients using artificial neural networks (ANN). We assessed 25 laboratory parameters at first from 248 consecutive adult COVID-19 patients for database creation, training, and development of ANN models. We developed a new alpha-index to assess association of each parameter with outcome. We used 166 records for training of computational simulations (training), 41 for documentation of computational simulations (validation), and 41 for reliability check of computational simulations (testing). The first five laboratory indices ranked by importance were Neutrophil-to-lymphocyte ratio, Lactate Dehydrogenase, Fibrinogen, Albumin, and D-Dimers. The best ANN based on these indices achieved accuracy 95.97%, precision 90.63%, sensitivity 93.55%. and F1-score 92.06%, verified in the validation cohort. Our preliminary findings reveal for the first time an ANN to predict ICU hospitalization accurately and early, using only 5 easily accessible laboratory indices

An adaptive delayed acknowledgment strategy to improve TCP performance in multi-hop wireless networks.

In multi-hop wireless networks, transmission control protocol (TCP) suffers from performance deterioration due to poor wireless channel characteristics. Earlier studies have shown that the small TCP acknowledgments consume as much wireless resources as the long TCP data packets. Moreover, generating an acknowledgment (ACK) for each incoming data packet reduces the performance of TCP. The main factor affecting TCP performance in multi-hop wireless networks is the contention and collision between ACK and data packets that share the same path. Thus, lowering the number of ACKs using the delayed acknowledgment option defined in IETF RFC 1122 will improve TCP performance. However, large cumulative ACKs will induce packet loss due to retransmission time-out at the sender side of TCP. Motivated by this understanding, we propose a new TCP receiver with an adaptive delayed ACK strategy to improve TCP performance in multi-hop wireless networks. Extensive simulations have been done to prove and evaluate our strategy over different topologies. The simulation results demonstrate that our strategy can improve TCP performance significantly

Genetic prediction of ICU hospitalization and mortality in COVID-19 patients using artificial neural networks

There is an unmet need of models for early prediction of morbidity and mortality of Coronavirus disease-19 (COVID-19). We aimed to a) identify complement-related genetic variants associated with the clinical outcomes of ICU hospitalization and death, b) develop an artificial neural network (ANN) predicting these outcomes and c) validate whether complement-related variants are associated with an impaired complement phenotype. We prospectively recruited consecutive adult patients of Caucasian origin, hospitalized due to COVID-19. Through targeted next-generation sequencing, we identified variants in complement factor H/CFH, CFB, CFH-related, CFD, CD55, C3, C5, CFI, CD46, thrombomodulin/THBD, and A Disintegrin and Metalloproteinase with Thrombospondin motifs (ADAMTS13). Among 381 variants in 133 patients, we identified 5 critical variants associated with severe COVID-19: rs2547438 (C3), rs2250656 (C3), rs1042580 (THBD), rs800292 (CFH) and rs414628 (CFHR1). Using age, gender and presence or absence of each variant, we developed an ANN predicting morbidity and mortality in 89.47% of the examined population. Furthermore, THBD and C3a levels were significantly increased in severe COVID-19 patients and those harbouring relevant variants. Thus, we reveal for the first time an ANN accurately predicting ICU hospitalization and death in COVID-19 patients, based on genetic variants in complement genes, age and gender. Importantly, we confirm that genetic dysregulation is associated with impaired complement phenotype

A new hybrid simulated annealing-based genetic programming technique to predict the ultimate bearing capacity of piles

The aim of this research is to develop three soft-computing techniques, including adaptive-neuro-fuzzy inference system (ANFIS), genetic-programming (GP) tree-based, and simulated annealing–GP or SA–GP for prediction of the ultimate-bearing capacity (Qult) of the pile. The collected database consists of 50 driven piles properties with pile length, pile cross-sectional area, hammer weight, pile set and drop height as model inputs and Qult as model output. Many GP and SA–GP models were constructed for estimating pile bearing capacity and the best models were selected using some performance indices. For comparison purposes, the ANFIS model was also applied to predict Qult of the pile. It was observed that the developed models are able to provide higher prediction performance in the design of Qult of the pile. Concerning the coefficient of correlation, and mean square error, the SA–GP model had the best values for both training and testing data sets, followed by the GP and ANFIS models, respectively. It implies that the neural-based predictive machine learning techniques like ANFIS are not as powerful as evolutionary predictive machine learning techniques like GP and SA–GP in estimating the ultimate-bearing capacity of the pile. Besides, GP and SA–GP can propose a formula for Qult prediction which is a privilege of these models over the ANFIS predictive model. The sensitivity analysis also showed that the Qult of pile looks to be more affected by pile cross-sectional area and pile set

Variations in treatment of C1 fractures by time, age, and geographic region in the United States: An analysis of 985 patients

The purpose of this investigation was to evaluate the variations in the treatment of C1 fractures over time, by age group, and by geographic region using a nationwide database. The Nationwide Emergency Department Sample (NEDS) database was queried to identify patients ≥18 years who sustained C1 fracture from 2006-2012. Patients were filtered based on the intervention they received: collar, halo, or surgery. Regions of hospital used in analysis were defined as Northeast, Midwest, South, and West. Surgical intervention for C1 fracture increased from 27.1% of cases in 2006 to 55.4% of cases in 2012 (P<0.001). The rate of collar treatment increased with increasing age. In contrast, rate of halo use decreased with increasing age. A greater proportion of patients in the Northeast were treated by collar compared to all other regions (P<0.001). We can conclude that there is considerable variation in the treatment of C1 fractures with regards to age and geographic region. Surgical treatment of these fractures is increasing over time. Future considerations should be given to developing treatment guidelines to decrease variation and potentially create cost-savings

Shaft resistance of bored piles socketed in Malaysian granite

In this paper, an attempt is made to demonstrate the applicability of previously published models for granite formations in Malaysia. A programme of field testing was conducted to measure the axial response of bored piles, with a total of seven bored piles of diameter 1000-1500 mm constructed in decomposed granite, using techniques including the advancing of temporary casing and drilling slurry composed of bentonite fluids. These bored piles were subjected to static load testing in order to verify their integrity and performance, with the results of these load tests evaluated here. It was assumed that the strength of the soil was fully mobilised by the static loading test. The results revealed that the method proposed by Horvath and Kenny provided the best prediction of rock shaft resistance for decomposed granite. Based on the results obtained, it can be deduced that the proposed models based on sedimentary rocks are applicable to decomposed granite and that maximum rock shaft resistance can mobilise up to 1850 kPa in material of rock quality designation >60%

Genetic programming and gene expression programming for flyrock assessment due to mine blasting

This research is aimed to develop new practical equations to predict flyrock distance based on genetic programming (GP) and genetic expression programming (GEP) techniques. For this purpose, 97 blasting operations in Delkan iron mine, Iran were investigated and the most effective parameters on flyrock were recorded. A database comprising of five inputs (i.e. burden, spacing, stemming length, hole depth, and powder factor) and one output (flyrock) was prepared to develop flyrock distance. Several GP and GEP models were proposed to predict flyrock considering the modeling procedures of them. To compare the performance prediction of the developed models, coefficient of determination (R2), mean absolute error (MAE), root mean squared error (RMSE) and variance account for (VAF) were computed and then, the best GP and GEP models were selected. According to the obtained results, it was found that the best flyrock predictive model is the GEP based-model. As an example, considering results of RMSE, values of 2.119 and 2.511 for training and testing datasets of GEP model, respectively show higher accuracy of this model in predicting flyrock, while, these values were obtained as 5.788 and 10.062 for GP model