When a nanomedicine is administrated into the human body, biomolecules in biological fluids, particularly proteins, form a layer on the surface of the nanoparticle known as a "personalized protein corona". An understanding of the formation and behavior of the personalized protein corona not only benefits the nanotherapy treatment efficacy but also can aid in disease diagnosis. Here we used Gd@ C 82 (OH) 22 nanoparticles, a nanomedicine effective against several types of cancer, as a model nanomedicine to investigate the natural protein fingerprint of the personalized protein corona formed in 10 human lung squamous cell carcinoma patients. Our analysis revealed a specific biomarker, complement component C1q, in lung cancer personalized protein coronas, abundantly bound to Gd@C 82 (OH) 22 NPs. This binding altered the secondary structure of C1q protein and led to the activation of an innate immune response, which could be exploited for cancer immune therapy. On the basis of this finding, we provide a new strategy for the development of precision nanomedicine derived from opsonization of a unique protein fingerprint within patients. This approach overcomes the common pitfall of protein corona formation and exploits the corona proteins to generate a precision nanomedicine and diagnostic tool.

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Background: Delivering plant extract at high loading with intact antioxidants and efficient skin permeation always remains a challenge. To address this, we prepared a stable gel formulation containing nanoethosomes loaded with Achillea millefolium L. (AM) extract for topical drug delivery.Method: The AM extract was tested at first for phytochemical analysis, antioxidant activity, total phenolic and flavonoid content, and FTIR examination. The nanoethosomes containing AM extract were synthesized and characterized by size, surface charge, and morphology, and entrapment efficiency (EE) was determined. The optimized nanoethosomes were then incorporated to develop a topical gel formulation and subjected to skin for permeation, pH, viscosity, and organoleptic evaluation for up to three months.Results: The AM ethanolic extract demonstrated 88% free radical scavenging activity and notable phenolic and flavonoid contents of up to 123 mg GAE/g and 42 mg QE/g, respectively. The optimized nanoethosomes encapsulated with AM extract (240 nm) were spherical in shape, with −31.1 mV of surface charge, and showed considerable entrapment efficiency (90%). Furthermore, the selected topical gel remained stable during the study period. The Exvivo permeation study of ethosomal gel showed the highest release percentage of 79.8%.Conclusion: The study concludes that topical gel loaded with nanoethosomes containing AM extract is an encouraging approach for topical drug delivery.

Phenylalanine, an amino acid, is a "building block" of protein. Phenylalanine is a component of food sources and also derived through supplementation. In current treatment, phenylalanine is prescribed as anti-depressant agent. The present study reviewed the possible antidepressant potential of phenylalanine. We reviewed data using the major databases, namely, Web of Science, SciFinder, Google Scholar, and PubMed. This manuscript provides a brief overview of the role of phenylalanine in depressive disorders. Phenylalanine possesses anti-depressant potential. Significant anti-depressant activities have been studied both in-vitro and in-vivo models. Based on current data, phenylalanine could be recommended as a potential candidate for clinical anti-depressant trials. Phenylalanine hydroxylase (PAH) deficiency results in intolerance to the dietetic consumption of the phenylalanine and a variety of syndromes such as deep and permanent logical disability, impaired cognitive development.

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Background The world is presently facing the challenges posed by COVID-19 (2019-nCoV), especially in the public health sector, and these challenges are dangerous to both health and life. The disease results in an acute respiratory infection that may result in pain and death. In Pakistan, the disease curve shows a vertical trend by almost 256K established cases of the diseases and 6035 documented death cases till August 5, 2020. Objective The primary purpose of this study is to provide the statistical model to predict the trend of COVID-19 death cases in Pakistan. The age and gender of COVID-19 victims were represented using a descriptive study. Method ology: Three regression models, which include Linear, logarithmic, and quadratic, were employed in this study for the modelling of COVID-19 death cases in Pakistan. These three models were compared based on R 2 , Adjusted R 2 , AIC, and BIC criterions. The data utilized for the modelling was obtained from the National Institute of Health of Pakistan from February 26, 2020 to August 5, 2020. Conclusion The finding deduced after the prediction modelling is that the rate of mortality would decrease by the end of October. The total number of deaths will reach its maximum point; then, it will gradually decrease. This indicates that the curve of total deaths will continue to be flat, i.e., it will shift to be constant, which is also the upper bound of the underlying function of absolute death.

Background: Monkeypox virus is gaining attention due to its severity and spread amongpeople. This study sheds light on the modeling and forecasting of new monkeypox cases. Knowledgeabout the future situation of the virus using a more accurate time series and stochastic models isrequired for future actions and plans to cope with the challenge. Methods: We conduct a side-by-sidecomparison of the machine learning approach with the traditional time series model. The multilayerperceptron model (MLP), a machine learning technique, and the Box–Jenkins methodology, alsoknown as the ARIMA model, are used for classical modeling. Both methods are applied to theMonkeypox cumulative data set and compared using different model selection criteria such as rootmean square error, mean square error, mean absolute error, and mean absolute percentage error.Results: With a root mean square error of 150.78, the monkeypox series follows the ARIMA (7,1,7)model among the other potential models. Comparatively, we use the multilayer perceptron (MLP)model, which employs the sigmoid activation function and has a different number of hidden neuronsin a single hidden layer. The root mean square error of the MLP model, which uses a single input andten hidden neurons, is 54.40, significantly lower than that of the ARIMA model. The actual confirmedcases versus estimated or fitted plots also demonstrate that the multilayer perceptron model has abetter fit for the monkeypox data than the ARIMA model. Conclusions and Recommendation: Whenit comes to predicting monkeypox, the machine learning method outperforms the traditional timeseries. A better match can be achieved in future studies by applying the extreme learning machinemodel (ELM), support vector machine (SVM), and some other methods with various activationfunctions. It is thus concluded that the selected data provide a real picture of the virus. If thesituations remain the same, governments and other stockholders should ensure the follow-up ofStandard Operating Procedures (SOPs) among the masses, as the trends will continue rising in theupcoming 10 days. However, governments should take some serious interventions to cope withthe virus. Limitation: In the ARIMA models selected for forecasting, we did not incorporate theeffect of covariates such as the effect of net migration of monkeypox virus patients, governmentinterventions, etc.