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As educators around the world are exploring new approaches to keep students involved in remote learning during the pandemic, we investigated the utility of memes in promoting engagement in the online environment. Medical students enrolled in a human physiology course at the College of Medicine and Health Sciences, Sohar, Oman were provided with an option to create memes related to the learning outcomes in renal physiology. One hundred forty-six of 280 students chose to create memes (52%), and the remaining students chose to submit either a labeled diagram or a concept map. Students uploaded their work in the discussion forum of the learning management system. All students enrolled in the course were given an opportunity for interaction with the uploaded content by commenting and upvoting thereafter. Students were requested to give anonymous feedback on their experience specifically on the activity related to memes. Feedback received from 142 of 280 students through anonymous comments was subjected to thematic analysis. Based on the analysis of the data, we found that memes elicited interest in the topic, facilitated peer interaction, simplified complex ideas, enhanced retention of associated concepts, and fostered a positive learning environment.

The adoption of cocrystals into oral pharmaceutical formulations is expected to propel this, as yet, moribund field into clinical and monetary prominence. The increase in resources and research that will inevitably follow is expected to address long standing problems in crystal engineering. Ab initio predictions of heterosynthon structure, identification of molecular descriptors to enable bioavailability enhancing coformer selection and a priori prediction of the cocrystal lattice have thus far been insoluble. Solutions to the prediction of supersaturation levels and polymorphic transition of the cocrystal formulated API in GI fluids, and solvation barrier and solubilization free energies, remain empirical. Properties such as on-demand/environment, formulation enabled, proton transfer between API and coformer and the development of neural networks or AI, trained on empirical data to predict ideal API-coformer pairs without the need to explicitly understand mechanisms, remain obscure, and hence un-researched. It is ironic that failure mechanisms that result in the delayed appearance of a less- soluble polymorph in pharmaceutical formulations (disappearing polymorphs) could (and should) be gainfully utilized to facilitate the delayed appearance in vivo of less-soluble polymorph(s) in cocrystal formulations. The bioavailability increase afforded by cocrystal formulations has the potential to enable greater patient compliance due to favorable posology, introduce more efficacious drugs into the therapeutic armamentarium, enable line- extensions and expand intellectual property portfolios, reduce the cost-of-goods; and hence of medicines, and add one more valuable tool in the repertoire of the pharmaceutical formulation scientist.

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Here we develop a tool to predict resectability of HER2+ breast cancer at breast conservation surgery (BCS) utilizing features identified on preoperative breast MRI. We identified patients with HER2+ breast cancer who obtained pre-operative breast MRI and underwent BCS between 2002-2013. From the contoured tumor on pre-operative MRI, shape, histogram, and co-occurrence and size zone matrix texture features were extracted. In univariate analysis, Spearmans correlation coefficient (Rs) was used to assess the correlation between each image feature and an endpoint (surgical re-excision). For multivariate modeling, we employed a support vector machine (SVM) method in a manner of leave-one-out cross-validation (LOOCV). Of 109 patients with HER2+breast cancer who underwent BCS, 39% underwent surgical re-excision. 62% had residual cancer at re-excision. In univariate analysis, solidity (Rs = -0.32, p = 0.009) and extent (Rs = -0.29, p = 0.019) were significantly associated with re-excision. Skewness in post-contrast 1, 2, and 3 (Rs = 0.25, p = 0.045; Rs = 0.30, p = 0.015; Rs = 0.28, p = 0.026) and kurtosis in post-contrast 1 (Rs = 0.26, p = 0.035) were also statistically significant. LOOCV-based SVM test achieved 74.4% specificity and 71.4% sensitivity when 21 features were used. Thus, tumor texture, histogram and morphological MRI features may assist surgical planning, encouraging wide margins or mastectomy in patients who may otherwise go on to re-excision.

Background & aimsSmoking, an independent risk factor for pancreatitis, accelerates the development of alcoholic pancreatitis. Alcohol feeding of mice induces up-regulation of spliced X-box binding protein 1 (XBP1s), which regulates the endoplasmic reticulum (ER) unfolded protein response and promotes cell survival upon ER stress. We examined whether smoking affects the adaptive mechanisms induced by alcohol and accelerates disorders of the ER in pancreatic acinar cells.MethodsWe studied the combined effects of ethanol (EtOH) and cigarette smoke extract (CSE) on ER stress and cell death responses in mouse and human primary acini and the acinar cell line AR42J. Cells were incubated with EtOH (50 mmol/L), CSE (20-40 μg/mL), or both (CSE+EtOH), and analyzed by immunoblotting, quantitative reverse-transcription polymerase chain reaction, and cell death assays. Some cells were incubated with MKC-3946, an inhibitor of endoplasmic reticulum to nucleus signaling 1 (ERN1, also called IRE1) that blocks XBP1s formation. Male Sprague-Dawley rats were fed isocaloric amounts of an EtOH-containing (Lieber-DeCarli) or control diet for 11 weeks and exposed to cigarette smoke or room air in an exposure chamber for 2 hours each day. During the last 3 weeks, a subset of rats received intravenous injections of lipopolysaccharide (LPS, 3 mg/kg per week) to induce pancreatitis or saline (control). Pancreatic tissues were collected and analyzed by histology and immunostaining techniques.ResultsIn AR42J and primary acini, CSE+EtOH induced cell death (necrosis and apoptosis), but neither agent alone had this effect. Cell death was associated with a significant decrease in expression of XBP1s. CSE+EtOH, but neither agent alone, slightly decreased adenosine triphosphate levels in AR42J cells, but induced oxidative stress and sustained activation (phosphorylation) of eukaryotic translation initiation factor 2 alpha kinase 3 (EIF2AK3, also called PERK) and increased protein levels of DNA damage inducible transcript 3 (DDIT3, also called CHOP). CHOP regulates transcription to promote apoptosis. Incubation of AR42J or primary mouse or human acinar cells with MKC-3946 reduced expression of XBP1s, increased levels of CHOP, and induced cell death. In rats fed an EtOH diet, exposure to cigarette smoke increased ER stress in acinar cells and sensitized the pancreas to LPS-induced pathology.ConclusionsCigarette smoke promotes cell death and features of pancreatitis in EtOH-sensitized acinar cells by suppressing the adaptive unfolded protein response signaling pathway. It also activates ER stress pathways that promote acinar cell death.

Photo-induced non-radiative energy dissipation is a potential pathway to induce structural-phase transitions in two-dimensional materials. For advancing this field, a quantitative understanding of real-time atomic motion and lattice temperature is required. However, this understanding has been incomplete due to a lack of suitable experimental techniques. Here, we use ultrafast electron diffraction to directly probe the subpicosecond conversion of photoenergy to lattice vibrations in a model bilayered semiconductor, molybdenum diselenide. We find that when creating a high charge carrier density, the energy is efficiently transferred to the lattice within one picosecond. First-principles nonadiabatic quantum molecular dynamics simulations reproduce the observed ultrafast increase in lattice temperature and the corresponding conversion of photoenergy to lattice vibrations. Nonadiabatic quantum simulations further suggest that a softening of vibrational modes in the excited state is involved in efficient and rapid energy transfer between the electronic system and the lattice.

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Musladin-Lueke syndrome (MLS), previously termed Chinese Beagle syndrome, is an autosomal-recessive connective tissue disorder characterized by extensive fibrosis of the skin and joints that was first identified in Beagles in the 1970s. Recent research identified a founder mutation (c.660C>T; p.R221C) in the ADAMTSL2 gene in Beagles with MLS. Here, we report the detailed clinical phenotype and laboratory findings in 2 Beagles affected with MLS. We discuss these findings in relation to the human disorder geleophysic dysplasia (GD), which also arises from recessive ADAMTSL2 mutations, and recent findings in Adamtsl2-deficient mice.

BackgroundThe Food and Drug Administration can reduce the nicotine content in cigarettes to very low levels. This potential regulatory action is hypothesised to improve public health by reducing smoking, but may have unintended consequences related to weight gain.MethodsWeight gain was evaluated from a double-blind, parallel, randomised clinical trial of 839 participants assigned to smoke 1 of 6 investigational cigarettes with nicotine content ranging from 0.4 to 15.8 mg/g or their own usual brand for 6 weeks. Additional analyses evaluated weight gain in the lowest nicotine content cigarette groups (0.4 and 0.4 mg/g, high tar) to examine the effect of study product in compliant participants as assessed by urinary biomarkers. Differences in outcomes due to gender were also explored.FindingsThere were no significant differences in weight gain when comparing the reduced nicotine conditions with the 15.8 mg/g control group across all treatment groups and weeks. However, weight gain at week 6 was negatively correlated with nicotine exposure in the 2 lowest nicotine content cigarette conditions. Within the 2 lowest nicotine content cigarette conditions, male and female smokers biochemically verified to be compliant on study product gained significantly more weight than non-compliant smokers and control groups.ConclusionsThe effect of random assignment to investigational cigarettes with reduced nicotine on weight gain was likely obscured by non-compliance with study product. Men and women who were compliant in the lowest nicotine content cigarette conditions gained 1.2 kg over 6 weeks, indicating weight gain is a likely consequence of reduced exposure to nicotine.Trial registration numberNCT01681875, Post-results.

Long-term pollutant concentration trends can be useful for evaluating air quality effects of emission controls and historical transitions in energy sources. We employed archival records of coefficient of haze (COH), a now-retired measure of light-absorbing particulate matter, to re-construct historical black carbon (BC) concentrations at urban locations in the United States (U.S.). The following relationship between COH and BC was determined by reinstating into service COH monitors beside aethalometers for two years in Vallejo and one year in San Jose, California: BC (μg m−3) = 6.7COH + 0.1, R2 = 0.9. Estimated BC concentrations in ten states stretching from the East to West Coast decreased markedly between 1965 and 1980: 5-fold in Illinois, Ohio, and Virginia, 4-fold in Missouri, and 2.5-fold in Pennsylvania. Over the period from the mid-1960s to the early 2000s, annual average BC concentrations in New Jersey and California decreased from 13 to 2 μg m−3 and 4 to 1 μg m−3, respectively, despite concurrent increases in fossil fuel consumption from 1.6 to 2.1 EJ (EJ = 1018 J) in New Jersey and 4.2 to 6.4 EJ in California. New Jersey's greater reliance on BC-producing heavy fuel oils and coal in the 1960s and early 1970s and subsequent transition to cleaner fuels explains why the decrease was larger in New Jersey than California. Patterns in seasonal and weekly BC concentrations and energy consumption trends together indicate that reducing wintertime emissions – namely substituting natural gas and electricity for heavy fuel oil in the residential sector – and decreasing emissions from diesel vehicles contributed to lower ambient BC concentrations. Over the period of study, declining concentrations of BC, a potent and short-lived climate warming pollutant, contrast increasing fossil fuel carbon dioxide (CO2) emissions in the U.S. Declining BC emissions may have had the benefit of mitigating some atmospheric warming driven by increased CO2 emissions with complementary health benefits.

Long-term pollutant concentration trends can be useful for evaluating air quality effects of emission controls and historical transitions in energy sources. We employed archival records of coefficient of haze (COH), a now-retired measure of light-absorbing particulate matter, to re-construct historical black carbon (BC) concentrations at urban locations in the United States (U.S.). The following relationship between COH and BC was determined by reinstating into service COH monitors beside aethalometers for two years in Vallejo and one year in San Jose, California: BC (μg m−3) = 6.7COH + 0.1, R2 = 0.9. Estimated BC concentrations in ten states stretching from the East to West Coast decreased markedly between 1965 and 1980: 5-fold in Illinois, Ohio, and Virginia, 4-fold in Missouri, and 2.5-fold in Pennsylvania. Over the period from the mid-1960s to the early 2000s, annual average BC concentrations in New Jersey and California decreased from 13 to 2 μg m−3 and 4 to 1 μg m−3, respectively, despite concurrent increases in fossil fuel consumption from 1.6 to 2.1 EJ (EJ = 1018 J) in New Jersey and 4.2 to 6.4 EJ in California. New Jersey's greater reliance on BC-producing heavy fuel oils and coal in the 1960s and early 1970s and subsequent transition to cleaner fuels explains why the decrease was larger in New Jersey than California. Patterns in seasonal and weekly BC concentrations and energy consumption trends together indicate that reducing wintertime emissions – namely substituting natural gas and electricity for heavy fuel oil in the residential sector – and decreasing emissions from diesel vehicles contributed to lower ambient BC concentrations. Over the period of study, declining concentrations of BC, a potent and short-lived climate warming pollutant, contrast increasing fossil fuel carbon dioxide (CO2) emissions in the U.S. Declining BC emissions may have had the benefit of mitigating some atmospheric warming driven by increased CO2 emissions with complementary health benefits.

PURPOSE: To use features extracted from magnetic resonance (MR) images and a machine-learning method to assist in differentiating breast cancer molecular subtypes. MATERIALS AND METHODS: This retrospective Health Insurance Portability and Accountability Act (HIPAA)-compliant study received Institutional Review Board (IRB) approval. We identified 178 breast cancer patients between 2006-2011 with: 1) ERPR + (n = 95, 53.4%), ERPR-/HER2 + (n = 35, 19.6%), or triple negative (TN, n = 48, 27.0%) invasive ductal carcinoma (IDC), and 2) preoperative breast MRI at 1.5T or 3.0T. Shape, texture, and histogram-based features were extracted from each tumor contoured on pre- and three postcontrast MR images using in-house software. Clinical and pathologic features were also collected. Machine-learning-based (support vector machines) models were used to identify significant imaging features and to build models that predict IDC subtype. Leave-one-out cross-validation (LOOCV) was used to avoid model overfitting. Statistical significance was determined using the Kruskal-Wallis test. RESULTS: Each support vector machine fit in the LOOCV process generated a model with varying features. Eleven out of the top 20 ranked features were significantly different between IDC subtypes with P < 0.05. When the top nine pathologic and imaging features were incorporated, the predictive model distinguished IDC subtypes with an overall accuracy on LOOCV of 83.4%. The combined pathologic and imaging models accuracy for each subtype was 89.2% (ERPR+), 63.6% (ERPR-/HER2+), and 82.5% (TN). When only the top nine imaging features were incorporated, the predictive model distinguished IDC subtypes with an overall accuracy on LOOCV of 71.2%. The combined pathologic and imaging models accuracy for each subtype was 69.9% (ERPR+), 62.9% (ERPR-/HER2+), and 81.0% (TN). CONCLUSION: We developed a machine-learning-based predictive model using features extracted from MRI that can distinguish IDC subtypes with significant predictive power. J. Magn. Reson. Imaging 2016;44:122-129.

PURPOSE: To investigate the association between a validated, gene-expression-based, aggressiveness assay, Oncotype Dx RS, and morphological and texture-based image features extracted from magnetic resonance imaging (MRI). MATERIALS AND METHODS: This retrospective study received Internal Review Board approval and need for informed consent was waived. Between 2006-2012, we identified breast cancer patients with: 1) ER+, PR+, and HER2- invasive ductal carcinoma (IDC); 2) preoperative breast MRI; and 3) Oncotype Dx RS test results. Extracted features included morphological, histogram, and gray-scale correlation matrix (GLCM)-based texture features computed from tumors contoured on pre- and three postcontrast MR images. Linear regression analysis was performed to investigate the association between Oncotype Dx RS and different clinical, pathologic, and imaging features. P

Whereas preclinical investigations and clinical studies have established that CD8+ T cells can profoundly affect cancer progression, the underlying mechanisms are still elusive. Challenging the prevalent view that the beneficial effect of CD8+ T cells in cancer is solely attributable to their cytotoxic activity, several reports have indicated that the ability of CD8+ T cells to promote tumor regression is dependent on their cytokine secretion profile and their ability to self-renew. Evidence has also shown that the tumor microenvironment can disarm CD8+ T cell immunity, leading to the emergence of dysfunctional CD8+ T cells. The existence of different types of CD8+ T cells in cancer calls for a more precise definition of the CD8+ T cell immune phenotypes in cancer and the abandonment of the generic terms "pro-tumor" and "antitumor." Based on recent studies investigating the functions of CD8+ T cells in cancer, we here propose some guidelines to precisely define the functional states of CD8+ T cells in cancer.

This paper explores some of the challenges that are involved in facilitating transformative learning. It presents a framework for practice that considers transformative learning from the perspective of the facilitator. These ideas were developed through a doctoral study in which adult educators were interviewed about their experiences in facilitating transformative learning. The framework comprises four components: confirming and interrupting current frames of reference, working with triggers for transformative learning, acknowledging a time of retreat or dormancy, and developing the new perspective. Using the four components of this framework for practice, I outline a series of questions for refection. Through detailed refection on aspects of program design and the interactions in the learning group, we can further our knowledge about the transformative aspects of our programs.