A Rare Presentation of Zoledronate-Induced Systemic Inflammatory Response.

Zoledronic acid is a bisphosphonate commonly used to treat various conditions involving bone loss. While it is generally well-tolerated, the occurrence of severe inflammatory reactions is rare. We present the case of an 82-year-old female who developed a severe immune reaction, including weakness and tenderness in her upper and lower extremities, following a single dose of zoledronic acid infusion for the treatment of osteoporosis. The onset of symptoms occurred one week after the infusion and persisted, progressively worsening over time, leading to functional impairment and the need for a walker for ambulation. Laboratory studies revealed an elevated erythrocyte sedimentation rate while other autoimmune markers were within normal limits. Differential diagnosis included an adverse reaction to zoledronic acid or underlying polymyalgia rheumatica. The patient showed significant improvement with a prednisone taper, suggesting an immune-mediated response. This case highlights the importance of considering severe immune reactions as a potential side effect of zoledronic acid and emphasizes the need for further research to better understand the underlying mechanisms and optimize patient management.

Unusual Etiology of Chronic Cough and Syncope as Chiari Malformation Type 1.

Chronic cough is a common chief complaint in ambulatory clinics. Unlike most cases that are caused by upper airway cough syndrome, gastroesophageal reflux disease, asthma, and non-asthmatic eosinophilic bronchitis, chronic cough can also be the presenting feature of a Chiari malformation. Our case is that of a 39-year-old female who had a chronic cough associated with shortness of breath, and when severe, associated with loss of consciousness. Her cough was refractory to conventional management. Further workup including pulmonary functions tests (PFT), laryngoscopy, high-resolution CT of the chest, an upper GI series, and esophageal pH manometry study were all normal. An MRI of her brain was obtained due to her syncopal episodes and revealed findings concerning a type 1 Chiari malformation. She subsequently underwent a Chiari decompression with patchy duraplasty and tonsilloplasty with cervical vertebrae 1 and 2 (C1-C2) laminectomy with a resolution of her symptoms. Chiari malformations are sometimes inherited but are often sporadic in nature, and, thus, appropriate diagnosis is key. Our patient is unique in that she presented at an older age, suggesting that atypical etiologies of a chronic cough refractory to conventional treatments must be considered.

Stock Portfolio Optimization Using a Combined Approach of Multi Objective Grey Wolf Optimizer and Machine Learning Preselection Methods.

The present paper deals with optimizing the stock portfolio of active companies listed on the Tehran Stock Exchange based on the forecast price. This paper is based on a combination of different filtering methods such as optimization of trading rules based on technical analysis (ROC, SMA, EMA, WMA, and MACD at six levels-Very Very Weak (VVW), Very Weak (VW), Weak (W), Strong (S), Very Strong (VS), and Very Very Strong (VVS)), Markov Chains, and Machine Learning (Random Forest and Support Vector Machine) Filter stock exchanges and provide buy signals between 2011 and 2020. In proportion to each combination of filtering methods, a buy signal is issued and based on the mean-variance (M-V) model, the stock portfolio is optimized based on increasing the portfolio return and minimizing the stock portfolio risk. Based on this, out of 480 companies listed on the Tehran Stock Exchange, 85 active companies have been selected and stock portfolio optimization is based on two algorithms, MOGWO and NSGA II. The analysis results show that the use of SVM learning machine leads to minor correlation error than the random forest method. Therefore, this method was used to predict stock prices. Based on the results, it was observed that if the shares of companies are filtered, the risk of transactions decreases, and the return on the stock portfolio increases. Also, if two filtering methods are applied simultaneously, the stock portfolio returns slightly and the risk increases. In the analysis, MOGWO algorithm has obtained 133.13% stock return rate with a risk of 3.346%, while the stock portfolio returns in NSGA II algorithm 107.73, with a risk of 1.459%. Comparison of solution methods shows that the MOGWO algorithm has high efficiency in stock portfolio optimization.

Brent Crude Oil Price Forecast Utilizing Deep Neural Network Architectures.

Brent crude oil is considered as one of the most important sources of crude oil pricing in the worldwide market, and it is used to set the price of two-thirds of the traded crude oil supplies in the world. To predict the price of Brent crude oil, LSTM and Bi-LSTM methods are applied, which are the architecture of the recursive neural network. Initially, the database creates the appropriate data for the period January 2015 to March 2021 from Brent crude oil price signals and daily data from a financial market, and then, the modeling process is performed via the use of MATLAB software. Also, about 90% of the data are for training and the remaining for validation and comparison. Using LSTM and Bi-LSTM neural networks, the network architecture has been worked on, and by adding the number of layers and changing the solvers (SGDM, RMSProp, and Adam), the errors of different models are compared with each other. Nonlinear techniques of artificial neural networks and deep learning were used for modeling. Then, the network architecture was worked on and the model error rate was evaluated by comparing different layers and solvents such as SGDM, RMSProp, and Adam. The superiority of SGDM solvent over others was shown, and finally, it can be mentioned as the superior method of modeling of price forecasting in Brent crude oil field. The results show that the model with two layers of LSTM and SGDM solver has less error and better accuracy.

Impact of Race and Socioeconomic Status on Outcomes in Patients Hospitalized with COVID-19.

BACKGROUND: The impact of race and socioeconomic status on clinical outcomes has not been quantified in patients hospitalized with coronavirus disease 2019 (COVID-19). OBJECTIVE: To evaluate the association between patient sociodemographics and neighborhood disadvantage with frequencies of death, invasive mechanical ventilation (IMV), and intensive care unit (ICU) admission in patients hospitalized with COVID-19. DESIGN: Retrospective cohort study. SETTING: Four hospitals in an integrated health system serving southeast Michigan. PARTICIPANTS: Adult patients admitted to the hospital with a COVID-19 diagnosis confirmed by polymerase chain reaction. MAIN MEASURES: Patient sociodemographics, comorbidities, and clinical outcomes were collected. Neighborhood socioeconomic variables were obtained at the census tract level from the 2018 American Community Survey. Relationships between neighborhood median income and clinical outcomes were evaluated using multivariate logistic regression models, controlling for patient age, sex, race, Charlson Comorbidity Index, obesity, smoking status, and living environment. KEY RESULTS: Black patients lived in significantly poorer neighborhoods than White patients (median income: $34,758 (24,531-56,095) vs. $63,317 (49,850-85,776), p < 0.001) and were more likely to have Medicaid insurance (19.4% vs. 11.2%, p < 0.001). Patients from neighborhoods with lower median income were significantly more likely to require IMV (lowest quartile: 25.4%, highest quartile: 16.0%, p < 0.001) and ICU admission (35.2%, 19.9%, p < 0.001). After adjusting for age, sex, race, and comorbidities, higher neighborhood income ($10,000 increase) remained a significant negative predictor for IMV (OR: 0.95 (95% CI 0.91, 0.99), p = 0.02) and ICU admission (OR: 0.92 (95% CI 0.89, 0.96), p < 0.001). CONCLUSIONS: Neighborhood disadvantage, which is closely associated with race, is a predictor of poor clinical outcomes in COVID-19. Measures of neighborhood disadvantage should be used to inform policies that aim to reduce COVID-19 disparities in the Black community.