How

Researchers out of Germany were curious to see how the brain responds to walking in nature versus walking in an urban environment, so they hooked 63 healthy people up to an fMRI machine and asked them to take a one-hour walk through either a forest or a shopping street. They found that activation of the amygdala (the brain region that triggers the fight-or-flight response) decreased after the one-hour nature walk :, but remained stable after the urban walk. "These results suggest that going for a walk in nature can have salutogenic effects on stress-related brain regions, and consequently, it may act as a preventive measure against mental strain and potentially disease,"  the study notes 

Good news for coffee drinkers emerged earlier this year, with studies reporting that drinking coffee is linked to a lower risk of mortality and that moderate daily coffee drinking may reduce kidney injury risk by 23%.

: Between  January 1, 2006, and December 31, 2010, the study recruited participants from the UK Biobank ages 40 to 69.

 The  study included 449,563 participants who were not diagnosed with cardiovascular problems at enrollment.  The participants had a median age of 58, and 55.3% were females. The  researchers asked the participants to self-report how many cups of coffee they drank each day and the type of coffee they usually drank via a touchscreen questionnaire.  The different coffee types, in order of popularity, were:

• instant coffee (44.1% of participants)
• ground coffee (18.4%)
• decaffeinated coffee (15.2%)

22.4% of the study population did not drink coffee and served as the comparator group. For each type of coffee, the researchers divided the study participants into 6 categories, depending on daily intake: 0, <1, 1, 2-3, 4-5, and >5 cups/day. 

 The  researchers followed up on the participants’ health status for 12.5 years and determined their health outcomes by looking at the ICD (International Classification of Diseases) codes on medical and death records.

 The study adjusted for factors that influence the risk of cardiovascular problems, including age, gender, alcohol intake, tea intake, obesity, diabetes, high blood pressure, obstructive sleep apnea, and smoking status.  The researchers then found that people who habitually drank ground, instant, or decaffeinated coffee had significantly lower risks of cardiovascular disease and death from any cause than non-coffee drinkers.

 The  researchers observed that consumption of 2-3 cups of coffee a day, regardless of the type of coffee, was consistently associated with the largest risk reduction in cardiovascular disease, coronary heart disease, congestive cardiac failure, and death from any cause 

Many of the recommendations for improving heart health focus on diet. 

 The  American Heart Association (AHA)Trusted Source recommends that people consume:

• a variety of vegetables and fruits 
• whole grains 
• lean  proteins, such as seafood and plant proteins from tofu and other sources 
•  liquid, nontropical oils, such as olive or avocado oil 
•  minimally processed foods 
•  no added sugars 
•  limited salt 
•  limited alcohol 

 There are a few specific diets have these  

Deep-Learning AI Tool Helps Detect Pancreatic Cancer 

 Among the various types of cancer, pancreatic cancer has the lowest five-year survival rate. It’s projected to become the second leading cause of cancerous death in the US by 2030. As with all types of cancer, early detection can be key in successfully treating the disease. Computed tomography (CT) is the method most commonly used in detecting pancreatic cancers, but it is not without its drawbacks. CT scans have only modest sensitivity in detecting small tumors; they miss approximately 40% of tumors smaller than 2 cm. CT scanning is also interpreter-dependent, which may be limited by availability and expertise of radiologists.

 A  nationwide, population-based study, published in the September issue of Radiology, sought to develop a tool to combat the acknowledged inefficiencies in CT scanning. Researchers in Taiwan developed a computer-aided detection (CAD) tool which included a segmentation convolutional neural network (CNN) to identify the pancreas on CT scans. Researchers also created an ensemble classifier, comprising five classification CNNs that predict whether pancreatic cancer is present. Researchers tested this tool on 669 patients with pancreatic cancer and a control group of 804 patients without pancreatic cancer.  The deep learning–based tool demonstrated 90% sensitivity and 93% specificity in real-world subjects. Furthermore, tests showed satisfactory sensitivity for tumors smaller than 2 cm at around 75%. “The CAD tool may serve as a supplement for radiologists to enhance the detection of pancreatic cancer,” said co-senior author, Wei-Chi Limo, MD, PhD, from National Taiwan University and National Taiwan University Hospital.