Graphic abstract Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure–activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure–activity relationship to drug repositioning, protein misfolding to protein–protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screeni...

Background While sarcopenia is typically defined using total psoas area (TPA), characterizing sarcopenia using only a single axial cross-sectional image may be inadequate. We sought to evaluate total psoas volume (TPV) as a new tool to define sarcopenia and compare patient outcomes relative to TPA and TPV. Method Sarcopenia was assessed in 763 patients who underwent pancreatectomy for pancreatic adenocarcinoma between 1996 and 2014. It was defined as the TPA and TPV in the lowest sex-specific quartile. The impact of sarcopenia defined by TPA and TPV on overall morbidity and mortality was assessed using multivariable analysis. Result Median TPA and TPV were both lower in women versus men (both P<0.001). TPA identified 192 (25.1 %) patients as sarcopenic, while TPV identified 152 patients (19.9 %). Three hundred sixty-nine (48.4 %) patients experienced a postoperative complication. While TPA-sarcopenia was not associated with higher risk of postoperative complications (OR 1.06; P=0.72), sarcopenia defined by TPV was associated with morbidity (OR 1.79; P=0.002). On multivariable analysis, TPV-sarcopenia remained independently associated with an increased risk of postoperative complications (OR 1.69; P=0.006), as well as long-term survival (HR 1.46; P=0.006). Conclusion The use of TPV to define sarcopenia was associated with both short- and long-term outcomes following resection of pancreatic cancer. Assessment of the entire volume of the psoas muscle (TPV) may be a better means to define sarcopenia rather than a single axial image.

Pancreatic cancer remains a deadly disease with a 5-year survival rate of only 8%. Even after surgical resection, most patients have recurrence of their cancer. Over the last 10 years, improvements in chemotherapy regimens led to a doubling in median overall survival. Here we review the management of advanced pancreatic cancer and highlight vaccine therapy as a novel modality of treatment.

Studying the complex molecular mechanisms involved in traumatic brain injury (TBI) is crucial for developing new therapies for TBI. Current treatments for TBI are primarily focused on patient stabilization and symptom mitigation. However, the field lacks defined therapies to prevent cell death, oxidative stress, and inflammatory cascades which lead to chronic pathology. Little can be done to treat the mechanical damage that occurs during the primary insult of a TBI; however, secondary injury mechanisms, such as inflammation, blood-brain barrier (BBB) breakdown, edema formation, excitotoxicity, oxidative stress, and cell death, can be targeted by therapeutic interventions. Elucidating the many mechanisms underlying secondary injury and studying targets of neuroprotective therapeutic agents is critical for developing new treatments. Therefore, we present a review on the molecular events following TBI from inflammation to programmed cell death and discuss current research and the latest therapeutic strategies to help understand TBI-mediated secondary injury.