The prognostic impact of clinical and molecular features in hairy cell leukaemia variant and splenic marginal zone lymphoma.

Hairy cell leukaemia variant (HCL-variant) and splenic marginal zone lymphoma (SMZL) are disorders with overlapping features. We investigated the prognostic impact in these disorders of clinical and molecular features including IGH VDJ rearrangements, IGHV gene usage and TP 53 mutations. Clinical and laboratory data were collected before therapy from 35 HCL-variant and 68 SMZL cases. End-points were the need for treatment and overall survival. 97% of HCL-variant and 77% of SMZL cases required treatment (P = 0·009). Survival at 5 years was significantly worse in HCL-variant [57% (95% confidence interval 38-73%)] compared with SMZL [84% (71-91%); Hazard Ratio 2·25 (1·20-4·25), P = 0·01]. In HCL-variant, adverse prognostic factors for survival were older age (P = 0·04), anaemia (P = 0·01) and TP 53 mutations (P = 0·02). In SMZL, splenomegaly, anaemia and IGHV genes with >98% homology to the germline predicted the need for treatment; older age, anaemia and IGHV unmutated genes (100% homology) predicted shorter survival. IGHV gene usage had no impact on clinical outcome in either disease. The combination of unfavourable factors allowed patients to be stratified into risk groups with significant differences in survival. Although HCL-variant and SMZL share some features, they have different outcomes, influenced by clinical and biological factors.

Point-and-shoot: rapid quantitative detection methods for on-site food fraud analysis – moving out of the laboratory and into the food supply chain

Major food adulteration and contamination events occur with alarming regularity and are known to be episodic, with the question being not if but when another large-scale food safety/integrity incident will occur. Indeed, the challenges of maintaining food security are now internationally recognised. The ever increasing scale and complexity of food supply networks can lead to them becoming significantly more vulnerable to fraud and contamination, and potentially dysfunctional. This can make the task of deciding which analytical methods are more suitable to collect and analyse (bio)chemical data within complex food supply chains, at targeted points of vulnerability, that much more challenging. It is evident that those working within and associated with the food industry are seeking rapid, user-friendly methods to detect food fraud and contamination, and rapid/high-throughput screening methods for the analysis of food in general. In addition to being robust and reproducible, these methods should be portable and ideally handheld and/or remote sensor devices, that can be taken to or be positioned on/at-line at points of vulnerability along complex food supply networks and require a minimum amount of background training to acquire information rich data rapidly (ergo point-and-shoot). Here we briefly discuss a range of spectrometry and spectroscopy based approaches, many of which are commercially available, as well as other methods currently under development. We discuss a future perspective of how this range of detection methods in the growing sensor portfolio, along with developments in computational and information sciences such as predictive computing and the Internet of Things, will together form systems- and technology-based approaches that significantly reduce the areas of vulnerability to food crime within food supply chains. As food fraud is a problem of systems and therefore requires systems level solutions and thinking.