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Critically ill patients are known to have a variety of electrolyte abnormalities. Lactic acidosis can frequently be seen secondary to shock states and is usually treated with aggressive volume resuscitation. Interestingly, hypophosphatemia is a potential cause of resistant lactic acidosis, which may not be as commonly identified or considered. We present a case of a 42-year-old man admitted twice over a span of 6 months with an elevated lactate level that did not resolve with volume resuscitation. It was ultimately determined that his lactic acidosis was due to hypophosphatemia after ruling out other potential causes. Phosphate replacement therapy resulted in the normalization of his lactate. In the literature, multiple theories have indicated the association of hypophosphatemia with lactic acidosis though no prior cases exist supporting a direct relationship. In this case, we set forth to evaluate the complicated relationship between all of these factors and to highlight the importance of early detection and treatment of hypophosphatemia, which may be beneficial in treating lactic acidosis.

Prosthetic valve thrombosis is a rare and severe complication of the mechanical prosthetic valve. Management can be challenging due to varying clinical presentation, overlapping features of differential diagnosis, and lack of randomized controlled trials on the therapeutic options. In this article, we report the case of a patient with a mechanical prosthetic mitral valve presented with symptoms of heart failure, and an echocardiography showing increased mean pressure gradient across the prosthesis along with a fixed posterior leaflet and a partially restricted anterior leaflet with no visible mass. That raised the concern for an obstructed prosthesis. After multimodality imaging and multidisciplinary team discussions, prosthetic valve thrombosis diagnosis was favored over other different diagnoses that included but not limited to pannus ingrowth. Fibrinolytic therapy was administrated, and the patient was discharged on optimal anticoagulation. Repeated echocardiography a month later showed normal mean gradient and normal functioning prosthetic mitral valve without the need for repeat mitral valve surgery.

Since the appearance of the E-Cigarette in the early 2000s, its industry, popularity, and prevalence have risen dramatically. In the past, E-Cigarette use with the vaping of nicotine or cannabis products had been associated with a few reported cases of lung injury. However, in 2019, thousands of cases of E-Cigarette or vaping product use-associated lung injury (EVALI) were reported in the United States. Evidence linked this outbreak with vaping of tetrahydrocannabinol (THC). We report two confirmed cases of EVALI and their associated clinical, radiologic, and pathologic features. This report supports the growing body of information regarding EVALI. It also discusses various substances, particularly vitamin E acetate, which has been suggested as a causative agent.

ABSTRACTBackground Infection with SARS-CoV-2 is a leading source of illness and death in the world. Millions of people contract SARS-CoV-2 every day as the incidence of infection rises. The mortality rate is mostly attributed to respiratory failure. Many studies have been carried out to identify biomarkers that can be used in the early detection of at-risk patients. CRP is a sensitive inflammatory biomarker; however, it can be measured by simple, inexpensive methods that are widely available in hospitals. Therefore, it was selected for this clinical trial.Methods This retrospective cohort analysis included 100 patients who were accepted to El-Obour Ain Shams University Specialized Hospital for Isolation between May and October 2020. Admission CRP was investigated, and data were analyzed in relation to severity and mortality.Results Regarding history, older patients or those who had been diagnosed with hypertension, diabetes mellitus, chronic hepatic diseases, or active cancer have been statistically more prone to mortality. Concerning the laboratory investigations, those who did not survive had significantly lower haemoglobin levels and a higher TLC count. In addition, serum ferritin and D-dimer levels were significantly higher in the non-survivors. As regards CRP, the non-survivor group had significantly higher levels, with a cutoff value of >129 mg/l to predict mortality. It has also been correlated with severity in terms of need for ICU admission and need for respiratory support, with a cutoff value of >55.3 mg/L.Conclusion CRP can be used as a prognostic biomarker in patients with SARS-CoV-2 infection as it is a simple and effective predictor.

ABSTRACTBackground Total knee arthroplasty (TKA) is a major surgical procedure that can be extremely upsetting. There are several methods available for postoperative pain management after knee surgery. One commonly used method is multimodal analgesia based on continuous adductor canal block.Aim of The Work Comparing the effect of the addition of posterior knee block, known as the infiltration between the popliteal artery and capsule of the knee (IPACK) block, to continuous adductor canal block (CACB) after total knee arthroplasty.Patients and Methods A total of 52 patients who underwent total knee arthroplasty were included and randomly divided into two groups receiving ultrasound-guided continuous adductor canal block or a combination of continuous ultrasound guided adductor canal block and the Infiltration between the popliteal artery and capsule of the knee block at the end of the surgery.Results Regarding the criteria for pain control, there was a statistically significant difference between the two groups regarding the postoperative visual analog score at 2, 4, 8, and 12 h after following up, which was not reported at 16 or 24 h postoperatively. The total nalbuphine consumption on the first day postoperative also showed a significant difference. A comparison of the basic demographic data, the onset of ambulation, the Timed Up and Go (TUG) test, and the straight leg raising test revealed no significant difference between the two groups.Conclusion Our study found that the use of continuous ultrasound guided adductor canal block and the Infiltration between the popliteal artery and capsule of the knee block together resulted in better postoperative pain management and reduced the need for nalbuphine. Despite this, there were no significant differences between the groups in terms of motor power or ambulation abilities. Trial registration number: PACTR202301536928551

Frequent itemset mining (FIM) is a crucial tool for identifying hidden patterns in information. FP-Growth is an FIM algorithm used to find associations. When the data size increases, the execution of FIM algorithms on a single machine suffers from computational problems, such as memory and time consumption. For these reasons, parallel and distributed processing on platforms such as Spark is essential. The parallel frequent pattern (PFP) is the implementation of FP-Growth in Spark. The main problem with PFP is that it does not consider the load balancing between cluster units. This research proposes an enhanced balanced parallel frequent pattern “EBPFP” algorithm to enhance and balance the PFP. The proposed algorithm (EBPFP) proposes two ideas. First, a strategy for load balancing between groups is proposed to ensure that the items are evenly divided between the nodes, and the cluster resources are used more effectively. Second, the improved conditional pattern base (ICPB) method aims to remove infrequent items from the conditional pattern base before constructing local FP-Trees. The experimental results show that the proposed EBPFP algorithm outperforms PFP, and the difference in running time between EBPFP and PFP was 21.56% and 39.72%, respectively.

Fake news detection (FND) involves predicting the likelihood that a particular news article (news report, editorial, expose, etc.) is intentionally deceptive. Arabic FND started to receive more attention in the last decade, and many detection approaches demonstrated some ability to detect fake news on multiple datasets. However, most existing approaches do not consider recent advances in natural language processing, i.e., the use of neural networks and transformers. This paper presents a comprehensive comparative study of neural network and transformer-based language models used for Arabic FND. We examine the use of neural networks and transformer-based language models for Arabic FND and show their performance compared to each other. We also conduct an extensive analysis of the possible reasons for the difference in performance results obtained by different approaches. The results demonstrate that transformer-based models outperform the neural network-based solutions, which led to an increase in the F1 score from 0.83 (best neural network-based model, GRU) to 0.95 (best transformer-based model, QARiB), and it boosted the accuracy by 16% compared to the best in neural network-based solutions. Finally, we highlight the main gaps in Arabic FND research and suggest future research directions.