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.

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...

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.

Purpose Several indices have been developed to predict overall survival (OS) in patients with breast cancer with brain metastases, including the breast graded prognostic assessment (breast-GPA), comprising age, tumor subtype, and Karnofsky performance score. However, number of brain metastases—a highly relevant clinical variable—is less often incorporated into the final model. We sought to validate the existing breast-GPA in an independent larger cohort and refine it integrating number of brain metastases. Patients and Methods Data were retrospectively gathered from a prospectively maintained institutional database. Patients with newly diagnosed brain metastases from 1996 to 2013 were identified. After validating the breast-GPA, multivariable Cox regression and recursive partitioning analysis led to the development of the modified breast-GPA. The performances of the breast-GPA and modified breast-GPA were compared using the concordance index. Results In our cohort of 1,552 patients, the breast-GPA was validated as a prognostic tool for OS (P < .001). In multivariable analysis of the breast-GPA and number of brain metastases (> three v ≤ three), both were independent predictors of OS. We therefore developed the modified breast-GPA integrating a fourth clinical parameter. Recursive partitioning analysis reinforced the prognostic significance of these four factors. Concordance indices were 0.78 (95% CI, 0.77 to 0.80) and 0.84 (95% CI, 0.83 to 0.85) for the breast-GPA and modified breast-GPA, respectively (P < .001). Conclusion The modified breast-GPA incorporates four simple clinical parameters of high prognostic significance. This index has an immediate role in the clinic as a formative part of the clinician's discussion of prognosis and direction of care and as a potential patient selection tool for clinical trials.

IMPORTANCE Currently, one of the most commonly available biomarkers in the treatment of patients with colorectal liver metastases (CRLM) is the Kirsten rat sarcoma viral oncogene homolog (KRAS); however, the prognostic implications of specific mutations of the KRAS gene are still not well defined. OBJECTIVE To investigate the prognostic impact of specific KRAS mutations on patients undergoing liver resection for CRLM. DESIGN, SETTING, AND PARTICIPANTS This retrospective single-center study was conducted from January 1, 2003, to December 31, 2013. Data about specific KRAS mutations for 331 patients who underwent hepatic resection for CRLM at Johns Hopkins Hospital between 2003 and 2013 were analyzed. Clinicopathological characteristics, perioperative details, and outcomes were stratified by specific KRAS mutation at codons 12 and 13. INTERVENTION Resection of CRLM. MAIN OUTCOMES AND MEASURES Overall survival (OS) and recurrence-free survival. RESULTS A mutated KRAS (mtKRAS) was identified in 91 patients (27.5%). At a median follow-up of 27.4 months, recurrence was observed in 48 patients (52.7%) with mtKRAS and 130 patients (54.2%) with wild-type KRAS (wtKRAS) (P = .82). Median and 5-year survival among patients with mtKRAS was 32.4 months and 32.7%, respectively, vs 58.5 months and 46.9%, respectively, for patients with wtKRAS (P = .02). Patients with KRAS codon 12 mutations had worse OS (hazard ratio [HR], 1.54; 95% CI, 1.05–2.27; P = .03) vs those with wtKRAS, whereas a KRAS codon 13 mutation was not associated with prognosis (HR, 1.47; 95% CI, 0.83–2.62; P = .19). Among the 6 most common mutations in codons 12 and 13, only G12V (HR, 1.78; 95% CI, 1.00–3.17; P = .05) and G12S (HR, 3.33; 95% CI, 1.22–9.10; P = .02) were associated with worse OS compared with patients with wtKRAS (both P < .05). Among patients who recurred, G12V (HR, 2.96; 95% CI, 1.32–6.61; P = .01), G12C (HR, 6.74; 95% CI, 2.05–22.2; P = .002), and G12S mutations (HR, 4.91; 95% CI, 1.52–15.8; P = .01) were associated with worse OS (both P < .05). CONCLUSIONS AND RELEVANCE G12V and G12S mutations of codon 12 were independent prognostic factors of worse OS. Among patients who recurred after resection of CRLM, G12V, G12C, and G12S mutations were associated with worse OS. Information on specific KRAS mutations may help individualize therapeutic and surveillance strategies for patients with resected CRLM.

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.

Malignant small bowel obstruction (MSBO) that does not resolve with conservative measures frequently leaves few treatment options other than palliative care. This single‐institution retrospective study assesses the outcomes of a more aggressive approach—concurrent systemic chemotherapy and total parenteral nutrition (TPN)—in the treatment of MSBO. The MD Anderson pharmacy database was queried to identify patients who received concurrent systemic chemotherapy and TPN between 2005 and 2013. Only patients with MSBO secondary to peritoneal carcinomatosis requiring TPN for ≥8 days were included. Survival and multivariate analyses were performed using the Kaplan–Meier method and Cox proportional hazard models. The study included 82 patients. MSBO resolution was observed in 10 patients. Radiographic assessments showed a response to chemotherapy in 19 patients; 6 of these patients experienced MSBO resolution. Patients spent an average of 38% of their remaining lives hospitalized, and 28% of patients required admission to the intensive care unit. In multivariate modeling, radiographic response to chemotherapy correlated with MSBO resolution (odds ratio [OR] 6.81; 95% confidence interval [CI], 1.68–27.85, P = 0.007). Median overall survival (OS) was 3.1 months, and the 1‐year OS rate was 12.6%. Radiographic response to chemotherapy (HR 0.30; 95% CI, 0.16–0.56, P < 0.001), and initiation of new chemotherapy during TPN (HR 0.55; 95% CI, 0.33–0.94, P = 0.026) independently predicted for longer OS. Concurrent treatment with systemic chemotherapy and TPN for persistent MSBO results in low efficacy and a high morbidity and mortality, and thus should not represent a standard approach.

Adoption of a more restrictive transfusion strategy in patients undergoing resection for cancer may preserve a limited resource, reduce costs, as well as avoid exposing oncology patients to the unnecessary risks associated with a transfusion.