Machine Learning-based Cerebral Venous Thrombosis Diagnosis with Clinical Data.

OBJECTIVES: Cerebral Venous Thrombosis (CVT) poses diagnostic challenges due to the variability in disease course and symptoms. The prognosis of CVT relies on early diagnosis. Our study focuses on developing a machine learning-based screening algorithm using clinical data from a large neurology referral center in southern Iran. METHODS: The Iran Cerebral Venous Thrombosis Registry (ICVTR code: 9001013381) provided data on 382 CVT cases from Namazi Hospital. The control group comprised of adult headache patients without CVT as confirmed by neuroimaging and was retrospectively selected from those admitted to the same hospital. We collected 60 clinical and demographic features for model development and validation. Our modeling pipeline involved imputing missing values and evaluating four machine learning algorithms: generalized linear model, random forest, support vector machine, and extreme gradient boosting. RESULTS: A total of 314 CVT cases and 575 controls were included. The highest AUROC was reached when imputation was used to estimate missing values for all the variables, combined with the support vector machine model (AUROC=0.910, Recall=0.73, Precision=0.88). The best recall was achieved also by the support vector machine model when only variables with less than 50% missing rate were included (AUROC=0.887, Recall=0.77, Precision=0.86). The random forest model yielded the best precision by using variables with less than 50% missing rate (AUROC=0.882, Recall=0.61, Precision=0.94). CONCLUSION: The application of machine learning techniques using clinical data showed promising results in accurately diagnosing CVT within our study population. This approach offers a valuable complementary assistive tool or an alternative to resource-intensive imaging methods.

Antibacterial potential of essential oils of Zataria multiflora and Mentha piperita, micro- and nano-formulated forms.

BACKGROUND AND OBJECTIVES: Plant-derived essential oils (EOs) shave many usages in health and medicine, such as antibacterial agents. The aim of this study was the improvement of antibacterial activities of two EOs using nanotechnology. MATERIALS AND METHODS: Antibacterial activity was investigated on four important human pathogenic bacteria using the 96-well plate microdilution method, a quantitative approach. Eleven formulations were prepared using each of the EOs. Eventually, the best nanoformulation with the smallest particle size and polydispersive indices (PDI and SPAN) was selected using each EO for further investigations. Moreover, two microemulsions with similar ingredients and the same portion in comparison with two selected nanoemulsions were also prepared. Antibacterial activity of each EO was compared with its micro- and nano-emulsions. RESULTS: The antibacterial efficacy of Zataria multiflora EO (ZMEO) was significantly better than Mentha piperita EO (MPEO). Besides, the antibacterial activity of nanoemulsion of ZMEO with a particle size of 129 ± 12 nm was significantly better than no- and micro-formulated forms of ZMEO. Interestingly, the efficiency of MPEO nanoemulsion (160 ± 25 nm) was also significantly better than MPEO and its micro-formulated form. CONCLUSION: Regardless of the intrinsic antibacterial property of two examined EOs, by formulating to nanoemulsion, their efficiencies were improved. Nanoemulsion of ZMEO introduced as an inexpensive, potent and green antibacterial agent.