The Millennium Summit in 2000 established the Millennium Development Goals (MDGs), which were agreed upon by 193 countries and 23 international organizations to combat extreme poverty and other pressing global priorities for human development. In the December 2011 issue of CPPM, Borda-Rodriguez and Huzair present an analysis of the close ties and synergies among the MDGs, pharmacogenomics and personalized medicine. Notably, MDGs promote the creation of collective innovation, a concept with both substantive and instrumental pertinence for the personalized medicine R&D that is currently undergoing rapid globalization. The ethos for collective innovation in global health is also embodied in the Paris Declaration on Aid Effectiveness, endorsed in 2005 by more than 100 signatories, including donor and developing country governments, regional development banks and international aid agencies.

Precision/personalized medicine is a hot topic in health care. Often presented with the motto “the right drug, for the right patient, at the right dose, and the right time,” precision medicine is a theory for rational therapeutics as well as practice to individualize health interventions (e.g., drugs, food, vaccines, medical devices, and exercise programs) using biomarkers. Yet, an alien visitor to planet Earth reading the contemporary textbooks on diagnostics might think precision medicine requires only two biomolecules omnipresent in the literature: nucleic acids (e.g., DNA) and proteins, known as the first and second alphabet of biology, respectively. However, the precision/personalized medicine community has tended to underappreciate the third alphabet of life, the “sugar code” (i.e., the information stored in glycans, glycoproteins, and glycolipids). This article brings together experts in precision/personalized medicine science, pharmacoglycomics, emerging technology governance, cultural studies, contemporary art, and responsible innovation to critically comment on the sociomateriality of the three alphabets of life together. First, the current transformation of targeted therapies with personalized glycomedicine and glycan biomarkers is examined. Next, we discuss the reasons as to why unraveling of the sugar code might have lagged behind the DNA and protein codes. While social scientists have historically noted the importance of constructivism (e.g., how people interpret technology and build their values, hopes, and expectations into emerging technologies), life scientists relied on the material properties of technologies in explaining why some innovations emerge rapidly and are more popular than others. The concept of sociomateriality integrates these two explanations by highlighting the inherent entanglement of the social and the material contributions to knowledge and what is presented to us as reality from everyday laboratory life. Hence, we present a hypothesis based on a sociomaterial conceptual lens: because materiality and synthesis of glycans are not directly driven by a template, and thus more complex and open ended than sequencing of a finite length genome, social construction of expectations from unraveling of the sugar code versus the DNA code might have evolved differently, as being future-uncertain versus future-proof, respectively, thus potentially explaining the “sugar lag” in precision/personalized medicine diagnostics over the past decades. We conclude by introducing systems scientists, physicians, and biotechnology industry to the concept, practice, and value of responsible innovation, while glycomedicine and other emerging biomarker technologies (e.g., metagenomics and pharmacomicrobiomics) transition to applications in health care, ecology, pharmaceutical/diagnostic industries, agriculture, food, and bioengineering, among others.

BACKGROUND: Improving stroke services is critical for reducing the global stroke burden. The World Stroke Organization-World Health Organization- Commission on Stroke conducted a survey of the status of stroke services in low and middle-income countries (LMICs) compared to high-income countries.

METHODS: Using a validated World Stroke Organization comprehensive questionnaire, we collected and compared data on stroke services along four pillars of the stroke quadrangle (surveillance, prevention, acute stroke, and rehabilitation) in 84 countries across World Health Organization regions and economic strata. The World Health Organization also conducted a survey of non-communicable diseases in 194 countries in 2019.

RESULTS: Fewer surveillance activities (including presence of registries, presence of recent risk factors surveys, and participation in research) were reported in low-income countries than high-income countries. The overall global score for prevention was 40.2%. Stroke units were present in 91% of high-income countries in contrast to 18% of low-income countries (p < 0.001). Acute stroke treatments were offered in ∼ 60% of high-income countries compared to 26% of low-income countries (p = 0.009). Compared to high-income countries, LMICs provided less rehabilitation services including in-patient rehabilitation, home assessment, community rehabilitation, education, early hospital discharge program, and presence of rehabilitation protocol.

CONCLUSIONS: There is an urgent need to improve access to stroke units and services globally especially in LMICs. Countries with less stroke services can adapt strategies from those with better services. This could include establishment of a framework for regular monitoring of stroke burden and services, implementation of integrated prevention activities and essential acute stroke care services, and provision of interdisciplinary care for stroke rehabilitation.

This paper introduces a new computer-aided classification system for detection of prostate cancer in
Transrectal Ultrasound images (TRUS). To increase the efficiency of the computer aided classification
process, an intensity adjustment process is applied first, based on the Pulse Coupled Neural Network
(PCNN) with a median filter. This is followed by applying a PCNN-based segmentation algorithm to
detect the boundary of the prostate image. Combining the adjustment and segmentation enable to eliminate PCNN sensitivity to the setting of the various PCNN parameters whose optimal selection can be difficult and can vary even for the same problem. Then, wavelet based features have been extracted and
normalized, followed by application of a rough set analysis to discover the dependency between the
attributes and to generate a set of reduct that contains a minimal number of attributes. Finally, a rough
confusion matrix is designed that contain information about actual and predicted classifications done by a
classification system. Experimental results show that the introduced system is very successful and has high detection accuracy